DEV Community: Jaideep Parashar

Most Developers Test Their Code. Why Don't They Test Their AI?

Jaideep Parashar — Fri, 21 Aug 2026 05:21:43 +0000

Here's something I've been thinking about while building AI systems:

Developers are obsessed with testing code.

We write unit tests.

Integration tests.

End-to-end tests.

CI pipelines.

Code reviews.

Linting.

Type checking.

But then we build an AI feature and suddenly the testing strategy becomes:

"I tried it three times and it seems pretty good."

That's not testing.

That's optimism.

And I think this is becoming one of the biggest weaknesses in AI development.

AI Applications Are Software

Consider a simple AI coding assistant.

The workflow might look like this:

User Request
↓
Context Retrieval
↓
Prompt
↓
LLM
↓
Generated Code
↓
Validation

Every component can fail.

The retrieval can return the wrong files.

The context can be incomplete.

The prompt can be ambiguous.

The model can hallucinate.

The generated code can contain bugs.

Yet many AI applications have no automated way of detecting these failures.

We wouldn't accept that standard from a normal API.

Why should AI be different?

"It Worked Once" Means Almost Nothing

Suppose you're building a system that converts natural language into SQL.

You test:

"Show me the top 10 customers by revenue."

The model generates:

SELECT customer_name, SUM(revenue) AS total_revenue
FROM sales
GROUP BY customer_name
ORDER BY total_revenue DESC
LIMIT 10;

Looks good.

You ship it.

Then a user asks:

"Show me the top 10 customers by revenue in 2025,
excluding cancelled orders."

Suddenly your system may produce completely different behavior.

AI outputs are probabilistic.

That means testing one input isn't enough.

Build an Evaluation Dataset

One of the simplest things an AI builder can do is create a small evaluation dataset.

For example:

test_cases = [
    {
        "input": "Find the top 10 customers by revenue.",
        "expected_contains": ["GROUP BY", "ORDER BY", "LIMIT"]
    },
    {
        "input": "Find revenue for 2025 excluding cancelled orders.",
        "expected_contains": ["2025", "cancelled"]
    },
]

Now you can run your AI system against the same cases whenever you change:

The prompt
The model
The retrieval system
The context
The workflow

That changes everything.

You're no longer asking:

"Does this feel better?"

You're asking:

"Did performance improve?"

Prompts Need Tests Too

This is one reason I don't think prompt engineering is disappearing.

A production prompt isn't just something you write once.

It is part of the system.

And if you change it, you should know whether the change improved the output.

I discussed the importance of this broader discipline in The Real Reason Prompt Engineering Isn't Going Away.

The next step is to connect prompt engineering with evaluation.

Think of it like software:

Prompt v1
↓
Evaluation
↓
Results
↓
Prompt v2
↓
Evaluation
↓
Compare

That's much more reliable than changing prompts based on intuition.

Context Needs Testing Too

Here's another problem.

You can have a perfect prompt and still get a terrible answer because the AI received the wrong context.

Imagine a coding assistant receives:

Prompt:
"Fix the authentication bug."

Context:

5 unrelated files
+
outdated documentation
+
wrong configuration

The model may generate perfectly reasonable code based on completely incorrect information.

This is why I believe context engineering is becoming just as important as prompt engineering.

I wrote about this in Why Context Engineering Is More Important Than Prompt Engineering.

The lesson is simple:

Don't only test what you ask the model. Test what you give the model.

Workflows Need Evaluation

This becomes even more important when AI is part of a larger workflow.

Consider:

User
↓
Retriever
↓
LLM
↓
Tool Call
↓
Validation
↓
Final Response

Where did the failure happen?

You need to know.

Was the retrieval wrong?

Did the model select the wrong tool?

Did the API return bad data?

Did validation fail?

This is one reason I've argued that workflows often matter more than agents.

A well-defined workflow gives you clear places to measure and debug.

I explored that argument in Why I Think Workflows Matter More Than Agents.

Start Small

You don't need an expensive AI evaluation platform to begin.

Start with 20–50 representative test cases.

For each case, record:

Input
Expected Behavior
Actual Output
Pass/Fail
Notes

Then run the dataset whenever you make a significant change.

Over time, your evaluation dataset becomes one of the most valuable assets in your AI project.

It captures what "good" actually means.

My AI Evaluation Rule

I've started thinking about AI systems in three layers:

Can it produce an answer?

Basic functionality.

Can it produce a correct answer consistently?

Reliability.

Can we measure whether it is improving?

Engineering maturity.

The third question is where many AI projects struggle.

If you cannot measure improvement, you're mostly guessing.

Evaluation Is the Missing Layer

The AI industry has spent enormous effort improving:

Models
Prompts
Agents
RAG
MCP
Vector databases

But evaluation deserves the same attention.

Because eventually every AI system needs to answer one uncomfortable question:

"How do you know it works?"

Not:

"The demo looked impressive."

Not:

"The model is highly capable."

Not:

"Users seem to like it."

Show me the evaluation.

That's the engineering mindset I want to see more often in AI.

Build This Into Your GitHub Workflow

I'm also adding a simple AI evaluation starter to my companion AI Builder resources.

A useful structure is:

ai-evaluation/
├── README.md
├── test_cases.json
├── evaluate.py
├── results.csv
└── prompts/
├── v1.txt
└── v2.txt

The idea is straightforward:

Prompt changes → Run tests → Record results → Compare versions.

This turns experimentation into an engineering process.

Final Thoughts

I don't think AI development should be:

Prompt
↓
Looks good
↓
Ship

It should look more like:

Build
↓
Evaluate
↓
Measure
↓
Improve
↓
Evaluate Again
↓
Deploy

That's how we build reliable software.

And I believe that's how we need to start building reliable AI.

The future of AI engineering won't belong only to people who know how to make models produce impressive outputs.

It will belong to developers who can measure, reproduce, debug, and improve those outputs.

Because the most important question in AI isn't:

"Can the model do it?"

It's:

"Can I prove that my system does it reliably?"

The 7 AI Repositories I Starred This Month

Jaideep Parashar — Mon, 17 Aug 2026 06:31:54 +0000

I don't star GitHub repositories just because they are popular.

A repository earns a star from me when I can see myself returning to it later.

Maybe it solves a real engineering problem.

Maybe it introduces a new architecture.

Maybe the code teaches me something.

Or maybe it represents where AI development is heading.

I've been spending a lot of time exploring AI repositories around agents, workflows, RAG, MCP, browser automation, model training, and API development.

These are seven repositories that stood out to me recently.

Not because you need all seven.

But because each one represents an important direction in AI development.

1. OpenAI Cookbook

Repository: https://github.com/openai/openai-cookbook

If you're building with the OpenAI API, this is one repository I would keep bookmarked.

The OpenAI Cookbook contains practical examples and guides covering common API development tasks, with many examples written in Python.

What I particularly like is the implementation-first approach.

Instead of spending hours reading theoretical explanations, you can study working examples and adapt them to your own application.

It's useful for:

API integration
Structured outputs
Embeddings
Agents
Evaluations
Multimodal applications

For beginners, it can also serve as a bridge between understanding an AI concept and actually implementing it.

2. LangChain

Repository: https://github.com/langchain-ai/langchain

LangChain remains one of the most important repositories in the LLM application ecosystem.

But I don't recommend it simply because it is popular.

I recommend understanding it because it exposes you to the building blocks behind modern AI applications.

Models.

Tools.

Retrievers.

Agents.

Integrations.

Structured outputs.

If you're serious about AI engineering, studying how these components fit together is valuable even if you eventually choose another framework.

3. LangGraph

Repository: https://github.com/langchain-ai/langgraph

This is probably one of the repositories I would recommend most strongly to developers interested in agentic systems.

LangGraph focuses on building stateful, controllable agents and long-running workflows.

What interests me most isn't the word "agent."

It's control.

AI systems become difficult when you don't know what happened during execution.

LangGraph provides concepts around state, persistence, human-in-the-loop interaction, and workflow orchestration.

That aligns closely with an idea I've written about before:

I believe workflows often matter more than agents.

I explored that argument in Why I Think Workflows Matter More Than Agents:

https://dev.to/jaideepparashar/why-i-think-workflows-matter-more-than-agents-3p82

4. LlamaIndex

Repository: https://github.com/run-llama/llama_index

If you're interested in RAG, document intelligence, or connecting LLMs to private data, LlamaIndex deserves your attention.

Its ecosystem provides tools for ingesting and structuring information from sources such as:

PDFs
APIs
Documents
SQL databases
Knowledge bases

The important idea here is simple:

An LLM becomes much more useful when it can work with your data.

This is also why I've become increasingly interested in context engineering rather than thinking only about prompt engineering.

The quality of the information supplied to the model can matter just as much as the wording of the instruction.

5. Browser Use

Repository: https://github.com/browser-use/browser-use

This is one of the more interesting repositories if you're thinking about AI agents interacting with the actual web.

Browser Use allows AI agents to control browsers and perform actions such as navigating websites, clicking elements, entering information, and extracting data.

That opens up a different category of AI applications.

Instead of:

User → Prompt → LLM → Text

You can build:

User
↓
AI Agent
↓
Browser
↓
Website
↓
Action
↓
Result

That's a significant shift.

The AI isn't merely generating an answer.

It's interacting with software.

6. Model Context Protocol Servers

Repository: https://github.com/modelcontextprotocol/servers

MCP has become one of the most interesting developments in the AI tooling ecosystem.

The official MCP servers repository contains reference implementations that demonstrate how AI applications can interact with external tools and data sources.

The important concept isn't any individual server.

It's the standardization.

AI systems can connect to external capabilities through a common protocol rather than requiring a completely different integration approach every time.

I've explored this topic in detail in 5 MCP Servers That Changed How I Build AI Workflows:

https://dev.to/jaideepparashar/5-mcp-servers-that-changed-how-i-build-ai-workflows-16j6

If you're building AI workflows in 2026, I think MCP is worth understanding.

7. Unsloth

Repository: https://github.com/unslothai/unsloth

The last repository on my list takes us in a different direction.

Unsloth focuses on running and training AI models locally, including workflows around fine-tuning and open models.

This matters because not every AI builder wants to depend entirely on hosted APIs.

There are legitimate reasons to explore local models:

Privacy
Cost control
Experimentation
Customization
Offline development
Model fine-tuning

The open-model ecosystem is becoming increasingly important, and projects like Unsloth make experimentation considerably more accessible.

What I Look For Before Starring a Repository

I don't use GitHub stars as a popularity contest.

When I evaluate an AI repository, I usually ask:

Does it solve a real problem?
Is the architecture worth studying?
Is the project actively maintained?
Can I learn something from the code?
Could I actually use it in a project?

If the answer is yes to several of these questions, the repository gets my attention.

That's also why I don't believe you need hundreds of AI repositories in your bookmarks.

You need the right ones.

Don't Build Your AI Stack From GitHub Stars

There is a trap here.

You discover an impressive repository.

Then another.

Before you know it, you're running ten frameworks, six databases, four agent libraries, and three orchestration systems.

That's not an AI stack.

That's dependency collection.

I've written about this problem in The Hidden Cost of Using Too Many AI Tools:

https://dev.to/jaideepparashar/the-hidden-cost-of-using-too-many-ai-tools-poo

The objective isn't to use more technology.

It's to build better systems.

Final Thoughts

What I like about GitHub is that it gives us something AI hype often doesn't:

the opportunity to inspect the work.

You can read the code.

Study the architecture.

Look at the issues.

Examine pull requests.

Run the examples.

And decide for yourself whether something is actually useful.

That's how I prefer to learn AI.

Not just by reading what people say about a technology.

By opening the repository and seeing what developers are actually building.

These seven repositories are a good starting point.

But don't just star them.

Clone one. Read the code. Build something. Break it. Fix it.

That's where the real learning begins.

About the Author

Jaideep Parashar is the Founder & Director of ReThynk AI Innovation and Research Pvt. Ltd., Six Sigma Black Belt, Lean Expert, AI Strategist, researcher, author, and keynote speaker. Through Agentic Process Excellence™, he helps organizations combine Artificial Intelligence, Lean Six Sigma, and systems thinking to build reliable, scalable, and continuously improving business operations.

Why Context Engineering Is More Important Than Prompt Engineering

Jaideep Parashar — Fri, 07 Aug 2026 05:20:40 +0000

For the past few years, prompt engineering has been one of the hottest topics in AI.

Countless tutorials have promised the "perfect prompt."

Courses have been built around writing better prompts.

Developers have spent hours refining wording to squeeze better responses from large language models.

But after building AI systems across multiple projects, I think the conversation is changing.

Prompt engineering is still important.

However, I believe context engineering is becoming even more important.

The difference may sound subtle.

In practice, it changes how we design AI applications.

Prompts Tell AI What to Do

A prompt is simply an instruction.

For example:

Generate a FastAPI CRUD application.

That's a prompt.

A better version might be:

Generate a FastAPI CRUD application.

Requirements:

Python 3.12
SQLAlchemy
Pydantic v2
JWT Authentication
Unit Tests
Docker Support

The second prompt is more specific.

It reduces ambiguity.

That's exactly why prompt engineering became so valuable.

In fact, I previously wrote The Real Reason Prompt Engineering Isn't Going Away, where I explain why better prompts continue to improve AI systems even as language models become more capable.

But modern AI applications require much more than better prompts.

Context Gives AI the Information It Needs

Imagine asking an AI assistant:

Summarize our API documentation.

Without context, the model has no documentation.

Now imagine the workflow:

User Request
↓
API Documentation
↓
GitHub Repository
↓
Coding Standards
↓
Previous Conversations
↓
LLM
↓
Response

Nothing about the prompt changed dramatically.

What changed was the context.

And that often has a much greater impact on output quality.

Modern AI Applications Run on Context

Today's AI systems rarely depend on prompts alone.

Instead, they combine information from multiple sources:

Retrieved documents
Vector databases
APIs
Tool outputs
Conversation history
User preferences
System instructions
Business rules

The model reasons over all of this information before producing a response.

That's context engineering.

Think Like a Software Engineer

Developers rarely write software by placing all their logic inside a single function.

Instead, they organize:

Modules
APIs
Databases
Configuration
Services

AI systems benefit from the same thinking.

Instead of creating one enormous prompt, we should design systems that assemble the right context automatically.

The prompt becomes only one component of a larger architecture.

Retrieval Is Context Engineering

Retrieval-Augmented Generation (RAG) is a perfect example.

Without retrieval:

Question
↓
LLM
↓
Answer

With retrieval:

Question
↓
Vector Database
↓
Relevant Documents
↓
LLM
↓
Answer

The improvement doesn't come from rewriting the prompt.

It comes from supplying better information.

MCP Is Expanding the Idea Even Further

Model Context Protocol (MCP) pushes this concept beyond documents.

Instead of retrieving only stored knowledge, AI systems can access:

GitHub repositories
Databases
File systems
APIs
Development tools

That dramatically expands the available context.

If you're exploring this direction, I recommend reading 5 MCP Servers That Changed How I Build AI Workflows, where I share the MCP servers that have had the biggest impact on my own projects.

Organized Context Beats Random Conversations

One mistake I see frequently is treating AI chats as permanent knowledge storage.

Useful prompts disappear.

Examples are lost.

Architecture decisions become impossible to find.

That's why I maintain structured prompt libraries with documentation, categories, and version history.

The goal isn't simply to organise prompts.

It's to organise reusable context.

I explained my complete workflow in How I Organize 10,000+ Prompts Across Projects.

Context Makes Workflows More Reliable

One reason I emphasize workflows is that they naturally manage context.

A good workflow knows:

What information to retrieve
Which tools to call
What data to validate
What output to generate

The AI model isn't making random decisions.

It's operating inside a structured system.

That's one reason I believe workflows often create more reliable AI applications than jumping directly to autonomous agents.

I discussed this idea further in Why I Think Workflows Matter More Than Agents.

Businesses Need Context Too

This idea extends beyond software engineering.

Organizations often invest in AI before understanding their own processes.

Without operational context, AI has little foundation to build upon.

Business rules.

Approval flows.

Compliance requirements.

Documentation.

These are all forms of organizational context.

Before implementing AI at scale, it's worth assessing whether that context already exists.

I explored this in AI Process Assessment: 9 Signs Your Business Is Ready for AI.

My Perspective

I don't think prompt engineering is disappearing.

Far from it.

Prompt engineering remains one of the foundational skills for working with large language models.

But I think the industry's focus is expanding.

The question is no longer:

"How do I write a better prompt?"

It's becoming:

"How do I provide the right information at the right time?"

That's a much bigger challenge.

And it's also a much more interesting engineering problem.

Final Thoughts

The future of AI won't be determined by who writes the cleverest prompts.

It will be shaped by those who design systems that deliver the right context consistently.

Great prompts still matter.

But great context transforms AI from an impressive chatbot into a dependable software system.

As builders, our goal shouldn't be to create longer prompts.

It should be to create smarter systems that know what information an AI model needs, and when it needs it.

That's why I believe context engineering is becoming one of the most valuable skills in modern AI development.

About the Author:

Jaideep Parashar is the Founder & Director of ReThynk AI Innovation and Research Pvt. Ltd., AI Strategist, researcher, author, Six Sigma Black Belt, and Lean Expert. He writes about practical AI implementation, Agentic Process Excellence™, and building reliable AI systems that combine technical innovation with operational excellence.

Website: ReThynk AI

References:

Why I Think Workflows Matter More Than Agents

Jaideep Parashar — Fri, 31 Jul 2026 05:36:16 +0000

If you've spent any time in the AI community recently, you've probably noticed one trend.

Everything is becoming an AI agent.

Need to summarize documents?

"Build an agent."

Need customer support?

"Use multiple agents."

Need a coding assistant?

"Deploy an autonomous agent with memory, tools, planning, and self-reflection."

Agents are exciting, and they absolutely have their place.

But after building AI applications and experimenting with different architectures, I've reached a different conclusion.

I think workflows matter more than agents.

That doesn't mean agents are bad.

It means I believe too many builders start by asking, "How can I build an agent?" instead of asking, "What's the simplest workflow that solves this problem?"

That single change in thinking has improved the way I design AI systems.

Workflows Solve Problems. Agents Execute Them.

Imagine you're building an AI-powered support system.

A workflow might look like this:

User Question
↓
Retrieve Knowledge
↓
Generate Response
↓
Validate Output
↓
Send Reply

Everything is predictable.

Each step has a clear purpose.

Now compare that with a system where an autonomous agent decides:

Which tools to use
Which documents to search
Whether to call another agent
Whether to retry
Whether to re-plan

Sometimes that's exactly what's needed.

But sometimes you've introduced far more complexity than the problem requires.

Complexity Has a Cost

Every additional layer in an AI system introduces new challenges.

More prompts
More APIs
More debugging
More monitoring
More failure points

A workflow with five deterministic steps is often easier to understand than an autonomous system making dozens of decisions behind the scenes.

That's one reason I previously wrote Why I Think Most AI Agents Are Overengineered, where I discuss how many projects adopt autonomous architectures long before they've demonstrated the need for them.

For many applications, simplicity isn't a limitation.

It's an advantage.

Predictability Beats Cleverness

One of the biggest benefits of workflows is predictability.

When something goes wrong, you know exactly where to look.

Was retrieval incorrect?
Did the prompt fail?
Did the API return an error?
Did validation reject the response?

Each stage can be tested independently.

With highly autonomous agents, tracing failures often becomes much harder because decision-making is distributed across multiple planning steps.

As systems grow, observability becomes just as important as intelligence.

Good Workflows Scale Better

I've learned that scaling isn't only about handling more users.

It's also about maintaining systems over time.

A clear workflow makes it easier to:

Add new features
Replace models
Update prompts
Improve evaluation
Introduce governance

Because each component has a defined responsibility.

That's much harder when responsibilities are spread across multiple autonomous agents.

Integration Matters More Than Autonomy

One lesson that has consistently shaped my AI stack is this:

Connected systems create more value than isolated intelligence.

A workflow that integrates GitHub, APIs, databases, and language models often delivers more business value than a sophisticated agent working in isolation.

That's one reason I've invested time in Model Context Protocol (MCP). Standardized integrations reduce friction and allow workflows to interact with external systems in a reliable way.

If you're exploring this approach, 5 MCP Servers That Changed How I Build AI Workflows covers the MCP servers that have had the biggest impact on my own projects.

The Best AI Stack Supports Workflows

When I look at my own development environment, I don't think about individual tools.

I think about how they work together.

ChatGPT.
Cursor.
FastAPI.
GitHub.
MCP.
Python.

Each tool has one clear responsibility within a larger workflow.

I described this philosophy in My Personal AI Stack in 2026, where I explain why I choose tools based on integration rather than popularity.

The stack exists to support the workflow—not the other way around.

Businesses Don't Need Agents First

One mistake I see organizations make is assuming that AI success starts with deploying agents.

In reality, it usually starts much earlier.

With understanding the process.

If the underlying workflow is inefficient, autonomous agents often automate inefficiency instead of eliminating it.

That's why I believe organizations should evaluate their operational readiness before investing heavily in advanced AI architectures.

I explored this in AI Process Assessment: 9 Signs Your Business Is Ready for AI, which provides practical indicators for assessing whether a business is prepared for AI adoption.

Similarly, Why You Should Fix Your Process Before Implementing AI explains why improving the process itself often delivers greater returns than introducing new AI technologies.

When Do Agents Make Sense?

This isn't an argument against agents.

There are many scenarios where they're the right choice.

For example:

Multi-step research
Dynamic task planning
Software engineering assistants
Long-running automation
Complex orchestration across multiple tools

But I treat agents as an optimization.

Not a starting point.

I first ask:

Can a workflow solve this?
Can it remain deterministic?
Can it be monitored easily?
Can another developer maintain it?

Only when the answer becomes "no" do I consider adding autonomous behavior.

Final Thoughts

AI agents will continue to improve.

Planning will become better.

Reasoning will become stronger.

Frameworks will become easier to use.

But I don't think the future belongs to the most autonomous systems.

I think it belongs to the teams that design the best workflows.

Because workflows create clarity.

Clarity creates reliability.

And reliable systems create real business value.

Before building another agent, it may be worth asking a simpler question:

Could a well-designed workflow solve this just as effectively?

About the Author:

Website: ReThynk AI

References:

10 Python Libraries Every AI Builder Should Know

Jaideep Parashar — Fri, 24 Jul 2026 05:01:26 +0000

If you're building AI applications in 2026, choosing the right Python libraries can save you hundreds of hours of development time.

The Python ecosystem has grown rapidly over the past few years. Every week, new frameworks promise to simplify AI development, but not every library deserves a place in your toolkit.

After building AI workflows, experimenting with different architectures, and integrating multiple AI services, I've found myself returning to the same set of libraries again and again.

These aren't necessarily the newest libraries.

They're the ones that consistently help me build reliable AI applications faster.

1. FastAPI

If I could recommend only one backend framework for AI applications, it would be FastAPI.

It provides:

High performance
Automatic API documentation
Type validation
Asynchronous support
Clean architecture

Whether you're exposing LLM endpoints, building AI agents, or creating internal APIs, FastAPI makes deployment straightforward.

It's the backbone of most of my AI projects.

2. LangChain

LangChain remains one of the most popular frameworks for developing LLM-powered applications.

It's particularly useful for:

Prompt templates
Tool integration
Retrieval-Augmented Generation (RAG)
Memory
Multi-step workflows

Although I don't use every feature it offers, it's still an excellent starting point for many AI applications.

3. Pydantic

Many developers think of Pydantic as just a validation library.

I think it's much more than that.

When AI applications exchange structured data between models, APIs, databases, and services, validation becomes essential.

Pydantic keeps those interactions predictable and significantly reduces runtime errors.

4. OpenAI SDK

Regardless of which models you use, understanding the official SDK is valuable.

It simplifies:

Chat completions
Streaming responses
Function calling
Structured outputs
Embeddings

Rather than building everything from raw HTTP requests, the SDK provides a clean developer experience.

5. ChromaDB

Almost every modern AI application eventually needs semantic search.

That's where vector databases become important.

ChromaDB offers a lightweight solution that's easy to integrate while learning concepts like:

Embeddings
Similarity search
Context retrieval
Retrieval-Augmented Generation (RAG)

For prototypes and many production use cases, it's an excellent starting point.

6. Pandas

AI isn't only about language models.

Many projects still require significant data preparation before an LLM becomes useful.

Pandas remains one of the best tools for:

Cleaning datasets
Data transformation
Feature preparation
CSV processing
Exploratory analysis

A good AI workflow almost always begins with good data.

7. NumPy

Underneath many AI libraries sits NumPy.

Even if you rarely interact with it directly, understanding arrays and numerical computation makes debugging and optimization much easier.

It's one of those foundational libraries every AI developer benefits from learning.

8. SQLAlchemy

Most AI applications eventually need persistence.

Whether you're storing conversations, prompts, user profiles, or evaluation results, SQLAlchemy provides a reliable way to interact with relational databases.

Keeping business logic separate from database logic makes AI systems easier to maintain as they grow.

9. Requests

Simple.

Reliable.

Still essential.

Despite the availability of specialized SDKs, I regularly use Requests for:

REST APIs
Webhooks
Third-party integrations
Internal microservices

Almost every AI workflow communicates with another service at some point.

Requests continues to be one of the easiest ways to make that happen.

10. Rich

Not every AI application needs a graphical interface.

Many run entirely from the terminal.

Rich makes command-line applications dramatically easier to use by adding:

Beautiful tables
Progress bars
Syntax highlighting
Status indicators
Better logging

For developer tools, it's one of my favorite quality-of-life libraries.

Libraries Matter Less Than Systems

Whenever someone asks me which Python library is the "best," my answer is usually the same.

The best library is the one that fits your workflow.

A stack filled with impressive frameworks won't help if the overall architecture becomes unnecessarily complex.

That's one reason I deliberately keep my own toolkit focused. In My Personal AI Stack in 2026, I explain how I choose tools based on workflow integration rather than popularity.

Similarly, I've learned that adding more libraries or AI tools doesn't automatically increase productivity. In The Hidden Cost of Using Too Many AI Tools, I discuss why simplifying your stack often leads to better long-term results.

Open Source Is One of the Greatest Advantages of AI Development

Many of the libraries in this article thrive because of active open-source communities.

Learning how these projects are built can teach you just as much as using them.

If you're looking for high-quality repositories to explore, I previously shared 7 GitHub Repositories I Recommend to Every AI Builder, featuring projects that have influenced the way I build AI applications.

Technology Alone Doesn't Guarantee Better AI

As AI projects mature, technical decisions become only one part of success.

Process design, governance, and organizational readiness become equally important.

Before scaling an AI initiative, it's worth asking whether the underlying business processes are actually ready. I explored that idea in AI Process Assessment: 9 Signs Your Business Is Ready for AI.

As AI systems become more integrated into business operations, governance also becomes essential. AI Governance Framework: A Practical Guide for Responsible AI Implementation outlines practical principles for building AI systems that are secure, compliant, and trustworthy.

Technology helps you build AI.

Good processes and governance help you build AI that lasts.

Final Thoughts

Python continues to be the language at the heart of modern AI development.

New libraries will emerge.

Existing ones will evolve.

Some will disappear.

But the goal isn't to learn every library.

It's to understand the role each one plays within a well-designed system.

The most effective AI builders aren't those with the largest toolbox.

They're the ones who know exactly when—and why—to use each tool.

About the Author:

Jaideep Parashar is the Founder & Director of ReThynk AI Innovation and Research Pvt. Ltd., a Six Sigma Black Belt, Lean Expert, AI Strategist, researcher, author, and keynote speaker. His work focuses on helping organizations combine Artificial Intelligence, Lean Six Sigma, and systems thinking through the discipline of Agentic Process Excellence™ to build reliable, scalable, and continuously improving business operations.

Reference:

My Personal AI Stack in 2026

Jaideep Parashar — Fri, 17 Jul 2026 09:48:36 +0000

Ask ten AI developers what tools they use, and you'll probably get ten different answers.

The AI ecosystem is evolving so quickly that it's easy to believe you need every new framework, model, and application to stay productive.

I don't think that's true.

Over the past year, I've experimented with dozens of AI tools while building products, writing technical content, managing prompt libraries, and developing AI workflows. Along the way, my stack has become surprisingly simple.

It's not built around the "best" tools.

It's built around the tools that work well together.

Here's the AI stack I rely on in 2026 and, more importantly, why each tool has earned its place.

1. ChatGPT: My Primary Thinking Partner

ChatGPT is where most of my work begins.

Not because it can do everything, but because it helps me think faster.

I use it for:

Brainstorming ideas
Structuring articles
Reviewing technical concepts
Exploring architectural trade-offs
Refining prompts
Research assistance

I rarely expect the first response to be perfect.

Instead, I treat it like collaborating with a knowledgeable teammate who accelerates my thinking.

2. Cursor: My AI-Powered Development Environment

When it's time to write code, I move into Cursor.

Its strength isn't just code generation.

It's understanding the context of an entire project.

Whether I'm building a FastAPI backend, integrating APIs, or refactoring an existing codebase, having AI directly inside the editor removes a huge amount of friction.

The less I switch between applications, the more productive I become.

In fact, one of the biggest lessons I've learned is that adding more AI tools doesn't automatically improve productivity. Sometimes it has the opposite effect. I explored this idea in The Hidden Cost of Using Too Many AI Tools, where I explain why a smaller, well-integrated stack often outperforms a collection of disconnected applications.

3. GitHub: The Source of Truth

Every project eventually ends up in GitHub.

Not just source code.

I also version:

Prompt templates
Documentation
API specifications
Experiment notes
Workflow diagrams

Treating AI assets like software assets has made collaboration and maintenance much easier.

GitHub isn't simply where my code lives.

It's where my AI knowledge evolves.

If you're building AI applications, I also recommend exploring several open-source repositories that have significantly improved my own workflow. I shared my favorites in 7 GitHub Repositories I Recommend to Every AI Builder.

4. MCP: Connecting Everything Together

The biggest change in my stack this year hasn't been a new language model.

It's been adopting Model Context Protocol (MCP).

Instead of manually copying information between applications, MCP allows AI systems to interact with repositories, documentation, databases, and external services through standardized interfaces.

Rather than thinking about individual AI tools, I now think about connected workflows.

That shift has been far more valuable than upgrading from one model to another.

If you're curious about practical implementations, I recently shared 5 MCP Servers That Changed How I Build AI Workflows, covering the MCP servers that have had the greatest impact on my projects.

5. A Structured Prompt Library

One of the least exciting parts of my stack is also one of the most valuable.

A structured prompt library.

Every useful prompt eventually becomes a reusable asset.

Instead of leaving prompts buried inside chat history, I organize them into categorized libraries that can be searched, refined, and reused across projects.

This has dramatically reduced duplicated work and improved consistency.

I shared the complete system in How I Organize 10,000+ Prompts Across Projects, including the principles I use to manage large prompt collections effectively.

6. FastAPI: The Backbone of My AI Applications

Whenever I need to expose AI capabilities through APIs, FastAPI is my preferred framework.

It provides:

Excellent performance
Automatic API documentation
Type validation
Clean architecture
Easy integration with AI services

Most AI applications eventually need reliable APIs.

FastAPI has consistently delivered that reliability.

7. Simple Workflows Over Complex Architectures

One trend I've intentionally avoided is building overly complicated AI systems.

Multiple agents.
Multiple orchestration frameworks.
Multiple reasoning layers.
Sometimes they're necessary.
Often they aren't.

I've found that a simple workflow that's easy to understand, maintain, and debug usually delivers more value than an impressive architecture that's difficult to operate.

Technology should reduce complexity.

Not introduce it.

My AI Stack Isn't Fixed

One thing people often ask is:

"What's the best AI stack?"

There isn't one.

My stack changes every few months.

Tools evolve.

Models improve.

New standards emerge.

What doesn't change is the philosophy behind it.

Every tool has to earn its place.

If it creates unnecessary complexity, overlaps with another tool, or doesn't improve my workflow, it doesn't stay.

The Principle That Guides Every Tool Choice

Over the past year, I've realized something important.

Successful AI builders don't optimize for the number of tools they use.

They optimize for the quality of the system they build.

That's also true at the organizational level.

Before investing in new AI platforms, businesses should first understand whether their processes are actually ready for AI adoption. I discussed this in AI Process Assessment: 9 Signs Your Business Is Ready for AI, which outlines practical indicators that help organizations evaluate their readiness before implementing AI.

Because the best AI stack isn't the one with the most tools.

It's the one that helps you deliver consistent results with the least unnecessary complexity.

Articles Reference:

The Hidden Cost of Using Too Many AI Tools

Jaideep Parashar — Fri, 10 Jul 2026 05:35:57 +0000

Every week, a new AI tool goes viral.

"This changes everything."

"The best AI coding assistant."

"The ultimate AI agent."

"The next ChatGPT killer."

As developers and AI builders, it's tempting to install every new tool that appears on GitHub or Product Hunt.

I've done exactly that.

But after building AI systems across multiple projects and experimenting with dozens of AI tools, I realized something unexpected.

The biggest productivity problem isn't having too few AI tools.

It's having too many.

The hidden cost isn't the subscription fee.

It's the complexity you introduce into your workflow.

More Tools Don't Always Mean More Productivity

Consider a typical AI workflow.

Research → ChatGPT

Coding → Cursor

Documentation → Claude

Automation → n8n

Images → Midjourney

Version Control → GitHub

None of these tools are bad.

In fact, they're excellent.

The problem appears when every task requires switching applications, changing context, and remembering different workflows.

Every tool has:

Different shortcuts
Different prompt styles
Different capabilities
Different limitations

Those small interruptions add up.

The result is fragmented attention instead of deep work.

Every New Tool Has a Hidden Learning Cost

Installing a new AI application takes minutes.

Learning to use it effectively takes much longer.

For every new platform you need to understand:

Configuration
Prompt behavior
Strengths
Weaknesses
Integrations
Best use cases

Now imagine doing that for fifteen different AI tools.

Eventually you're spending more time learning software than solving problems.

I've learned that mastering a small number of tools often creates far more value than constantly chasing new ones.

Build a Workflow, Not a Tool Collection

One mistake I see frequently is people comparing AI tools only by features.

Questions like:

Which model is fastest?
Which has the largest context window?
Which writes better code?

Those questions matter.

But I think a more important question is:

Does this tool improve my workflow?

A slightly less capable tool that integrates perfectly into your development process is often more valuable than a cutting-edge model that creates friction every day.

Integration Is Becoming More Important Than Features

Modern AI isn't just about language models.

It's about connected systems.

For example:

GitHub
↓
MCP Server
↓
LLM
↓
FastAPI
↓
Deployment

Instead of constantly copying information between applications, AI can interact directly with repositories, databases, APIs, and development environments.

That's one reason I've become increasingly interested in Model Context Protocol (MCP).

If you're exploring MCP, I recently shared 5 MCP Servers That Changed How I Build AI Workflows, covering the servers that have had the biggest impact on my own development process.

Your Prompt Library Shouldn't Live Inside Chat History

Another hidden cost of using too many AI tools is prompt duplication.

The same prompt ends up living in:

ChatGPT
Claude
Cursor
Notes
Random Markdown files

Soon you don't know which version is current.

That's why I stopped treating prompts as conversations.

I started treating them as reusable software assets.

Today I maintain structured prompt libraries with documentation, version history, and categories.

I explained the complete system in How I Organize 10,000+ Prompts Across Projects, where I share the workflow I use to manage large prompt libraries across multiple AI initiatives.

Complexity Grows Faster Than You Expect

Let's compare two architectures.

Workflow A

LLM
↓
FastAPI
↓
GitHub
↓
Deployment

Workflow B

Three LLMs
↓
Four AI Agents
↓
Five MCP Servers
↓
Vector Database
↓
Automation Platform
↓
Monitoring
↓
Deployment

The second system isn't automatically better.

It simply has more moving parts.

Every additional dependency introduces:

Configuration
Maintenance
Updates
Monitoring
Debugging

Complexity should solve a problem.

Not become one.

That's one reason I previously argued in Why I Think Most AI Agents Are Overengineered that many builders introduce autonomous agents before proving they actually need them.

Process Comes Before Platform

One lesson has repeated itself across almost every AI project I've worked on.

Organizations spend weeks comparing AI tools.

But they spend very little time improving the underlying workflow.

That's backwards.

The process should determine the technology.

Not the other way around.

I've seen companies purchase expensive AI platforms while leaving inefficient business processes untouched.

Predictably, the results fall short of expectations.

I explored this in more detail in Why You Should Fix Your Process Before Implementing AI, where I explain why process improvement should happen before AI implementation.

If you're interested in taking that idea even further, How Lean Six Sigma AI Create Better Business Processes explores how structured improvement methodologies can strengthen AI initiatives rather than simply automate existing inefficiencies.

My Rule for Adopting a New AI Tool

Before adding any new AI application to my workflow, I ask four simple questions.

Does it solve a real problem?
Can an existing tool already do this?
Will it simplify my workflow?
Will I still be using it six months from now?

If the answer is mostly "no," I don't install it.

Missing the latest trend is usually less expensive than managing unnecessary complexity.

Final Thoughts

The AI ecosystem will continue to grow.

New models will appear.

New frameworks will launch.

New startups will promise revolutionary productivity.

That's exciting.

But I've learned that productivity doesn't come from using the most AI tools.

It comes from building the right AI system.

The builders who create lasting value won't be the ones trying every new release.

They'll be the ones who understand their workflows, organize their knowledge, and choose tools intentionally.

Sometimes the smartest productivity improvement isn't adding another AI tool.

It's removing one.

Articles Reference:

Graphics Credit: Graphics designed by Vista Liberata | visit here

What I Learned After Building AI Systems Across Multiple Brands

Jaideep Parashar — Mon, 06 Jul 2026 09:20:03 +0000

One of the biggest misconceptions about AI is that every project is unique.

At first glance, it certainly feels that way.

One project is a chatbot.

Another is an AI-powered search system.

Another automates documentation.

Another generates code.

But after building AI systems across multiple brands and initiatives, I started noticing something surprising.

The technology changes.

The business domain changes.

The users change.

The underlying principles rarely do.

Here are some of the biggest lessons I've learned.

1. AI Doesn't Fix Broken Systems

Many teams believe AI will solve operational problems.

In reality, AI usually exposes them.

If documentation is inconsistent, AI becomes inconsistent.

If data is outdated, AI produces outdated answers.

If workflows are unclear, automation becomes unreliable.

One of the biggest lessons I've learned is this:

AI amplifies the quality of your existing systems.

It rarely compensates for poor foundations.

That's why I spend far more time understanding processes than choosing models.

2. Simplicity Beats Complexity

Every new AI framework looks exciting.

Agents.

Memory.

Planning.

Reflection.

Tool calling.

Multi-agent orchestration.

I've experimented with many of these approaches, but one principle keeps proving itself.

The simplest solution that solves the problem is usually the best solution.

A straightforward workflow is often easier to:

Build
Test
Maintain
Scale
Explain

Complexity should be introduced only when it delivers clear value.

3. Prompt Libraries Are More Valuable Than Individual Prompts

When I first started using AI, I wrote prompts from scratch.

Eventually I realized I was solving the same problems repeatedly.

Now I build prompt libraries.

Instead of creating new prompts every day, I improve existing ones.

This creates consistency across projects.

If you're interested in how I manage this, I recently shared the system I use to organize more than 10,000 prompts across different projects.

The shift from individual prompts to reusable prompt assets has had a much bigger impact than switching between AI models.

4. Workflows Matter More Than Models

Every few months a new model becomes available.

Larger context windows.

Lower latency.

Better reasoning.

Those improvements are exciting.

But in production systems, I rarely see the model as the primary challenge.

The bigger questions are:

Where does data come from?
What happens after generation?
How are errors handled?
Which tools should AI call?
What should happen if a request fails?

These workflow decisions usually have a greater impact on reliability than changing models.

5. Context Is the Real Product

People often ask:

"Which AI model should we use?"

I usually ask a different question.

"What information does the model need to succeed?"

Consider this coding request:

Write a Python authentication API.

Now compare it with:

Build a FastAPI authentication service.

Requirements:

Python 3.12
JWT authentication
SQLAlchemy
Pydantic v2
Password hashing with bcrypt
Refresh tokens
Unit tests
Docker support

Same model.

Different context.

Much better output.

The difference isn't intelligence.

It's information.

6. AI Adoption Is Mostly a Human Challenge

Technology is rarely the biggest obstacle.

People are.

Successful AI projects require:

Clear communication
Shared expectations
Good documentation
Process discipline
Continuous improvement

Without these, even the most advanced AI systems struggle to deliver long-term value.

7. Documentation Is a Competitive Advantage

Every AI project generates knowledge.

Prompt patterns.

Architecture decisions.

API conventions.

Lessons learned.

Common failures.

If that knowledge isn't documented, teams repeat the same mistakes.

I've found that good documentation often improves AI projects more than adding another framework.

Knowledge compounds.

Only if it's captured.

8. Every AI Project Becomes a Software Project

Many AI prototypes begin with a single prompt.

But successful ones quickly evolve into software.

Soon you need:

APIs
Authentication
Logging
Monitoring
Testing
Version control
Deployment pipelines

At that point, software engineering practices become just as important as prompt engineering.

Treating AI applications like production software changes how you build them.

9. Continuous Improvement Beats Perfect Design

I've stopped trying to design perfect AI systems on day one.

Instead, I focus on continuous improvement.

Build.

Measure.

Learn.

Improve.

Small iterations consistently outperform large redesigns.

This mindset has made AI projects far more sustainable.

My Biggest Takeaway

Across every brand and every AI initiative, one lesson keeps repeating itself.

Success rarely comes from having the newest model.

It comes from building reliable systems around the model.

Models will continue to improve.

Frameworks will continue to evolve.

New tools will appear every month.

Strong systems remain valuable regardless of which model powers them.

That's where I now spend most of my attention.

Final Thoughts

Building AI systems across multiple projects has changed the way I think about artificial intelligence.

I no longer see AI as a collection of prompts or models.

I see it as a system made up of people, processes, data, workflows, and software.

The model is important.

But it's only one piece of the puzzle.

The builders who create lasting value won't necessarily be the ones using the newest AI model.

They'll be the ones designing the most reliable systems around it.

And I believe that's where the next generation of AI innovation will come from.

How I Organize 10,000+ Prompts Across Projects

Jaideep Parashar — Fri, 03 Jul 2026 03:45:48 +0000

One question I get surprisingly often is:

"How do you manage thousands of AI prompts without losing track of them?"

The answer is simple.

I don't treat prompts as conversations.

I treat them as reusable software assets.

Over the years, I've created prompt libraries across multiple AI projects, books, research initiatives, and client work. That means managing well over 10,000 prompts covering everything from Python development and AI agents to content generation and workflow automation.

If you're still storing prompts in random ChatGPT conversations, you're making life much harder than it needs to be.

Here's the system that works for me.

Stop Thinking of Prompts as Temporary

Most people write a prompt, get an answer, and move on.

That's fine for casual use.

But builders rarely solve the same problem only once.

If you find yourself writing:

API documentation
SQL queries
FastAPI endpoints
Docker configurations
Code reviews
Git commit messages

...you're probably solving recurring problems.

Recurring problems deserve reusable prompts.

My Folder Structure

Instead of organizing prompts by AI tool, I organize them by purpose.

For example:

AI-Prompts/
│
├── Python/
│ ├── FastAPI
│ ├── Django
│ ├── Flask
│ └── Automation
│
├── JavaScript/
│ ├── React
│ ├── Node.js
│ └── TypeScript
│
├── DevOps/
│ ├── Docker
│ ├── Kubernetes
│ └── GitHub Actions
│
├── AI/
│ ├── RAG
│ ├── Agents
│ ├── MCP
│ └── Prompt Engineering
│
└── Documentation/

This mirrors how software projects are organized.

Finding a prompt takes seconds.

Every Prompt Has Metadata

A prompt isn't just text.

It's documentation.

Each prompt in my library includes:

Category:
Purpose:
Model:
Input:
Expected Output:
Version:
Last Updated:

For example:

Category:
FastAPI

Purpose:
Generate CRUD endpoints

Model:
GPT-4o

Expected Output:
Production-ready FastAPI code

Six months later, I know exactly why that prompt exists.

I Version My Prompts

Developers version code.

Why not prompts?

For example:

FastAPI_CRUD_v1.md

FastAPI_CRUD_v2.md

FastAPI_CRUD_v3.md

Sometimes a newer prompt performs better.

Sometimes it doesn't.

Versioning lets me compare results instead of rewriting everything from scratch.

I Separate Generic and Project-Specific Prompts

This has saved me countless hours.

Generic prompts:

Explain Python errors
Generate SQL
Optimize Dockerfiles
Write unit tests

Project prompts:

My authentication API
Internal coding standards
Company architecture
Deployment pipeline

Keeping these separate makes prompts reusable across different projects.

Context Matters More Than Length

One mistake I often see is assuming longer prompts are automatically better.

They're not.

A prompt should include only the context needed to complete the task.

Instead of:

Write some Python code.

I write:

Generate a FastAPI endpoint.

Requirements:

Python 3.12
Async support
SQLAlchemy
Pydantic v2
JWT authentication
Error handling
Unit tests

The improvement comes from clarity not verbosity.

If you're interested in why I believe this skill is still essential, I recently shared my thoughts in my recent article: The Real Reason Prompt Engineering Isn't Going Away.

Prompts Become Building Blocks

I rarely write prompts from scratch anymore.

Instead, I compose them.

For example:

Base Prompt
+
Coding Standards
+
Architecture Rules
+
Current Task
=
Final Prompt

This is surprisingly similar to software engineering.

Small reusable components create larger systems.

My Prompts Live in Git

Yes.

I store prompts in Git repositories.

Why?

Because Git gives me:

Version history
Branching
Collaboration
Backup
Change tracking

Prompts are part of the project.

They deserve the same treatment as code.

Documentation Is Part of the Prompt

Every important prompt includes:

What problem it solves
When to use it
When not to use it
Example input
Example output

That makes the prompt reusable for months, or even years.

Good Prompts Create Better Systems

One thing I've learned is that prompt quality directly affects system quality.

A reliable AI workflow starts with reliable instructions.

The same principle applies whether you're building:

AI coding assistants
RAG applications
MCP-powered workflows
Multi-agent systems

Good prompts reduce ambiguity.

Clear systems reduce maintenance.

Final Thoughts

Managing thousands of prompts isn't about having a better memory.

It's about having a better system.

The moment you start treating prompts like reusable assets instead of disposable conversations, your workflow changes.

You spend less time rewriting.

Less time searching.

Less time guessing.

And more time building.

For me, prompt management isn't an organizational habit.

It's part of software engineering.

As AI becomes a permanent part of development, I believe prompt libraries will become just as valuable as code libraries.

5 MCP Servers That Changed How I Build AI Workflows

Jaideep Parashar — Mon, 29 Jun 2026 03:53:41 +0000

Over the past year, one concept has fundamentally changed how I think about AI applications.

Not larger language models.

Not better prompts.

Not even AI agents.

It's Model Context Protocol (MCP).

For a long time, most AI applications lived inside a closed environment. They could generate text, answer questions, or write code, but they couldn't easily interact with external systems.

MCP changes that.

It provides a standardized way for AI models to communicate with tools, databases, APIs, and applications.

Instead of building custom integrations for every project, developers can expose capabilities through MCP servers.

After experimenting with different workflows, these are five MCP servers that have had the biggest impact on how I build AI applications.

1. GitHub MCP Server

If you're building software with AI, GitHub integration is one of the most valuable capabilities you can add.

Imagine asking an AI assistant to:

Read a repository
Review pull requests
Search issues
Create commits
Open new issues
Inspect project structure

Instead of manually copying files into ChatGPT, the AI can interact directly with your repository.

For developers, this dramatically improves productivity.

Typical workflow:

Developer Request
↓
GitHub MCP Server
↓
Repository
↓
LLM
↓
Action or Response

This is far more scalable than copying snippets of code into prompts.

2. Filesystem MCP Server

Almost every AI workflow eventually needs access to local files.

Examples include:

Reading documentation
Editing Markdown
Creating reports
Refactoring code
Updating configuration files

Without an MCP server, these tasks often require multiple manual steps.

With a Filesystem MCP server, an AI application can safely interact with project directories.

For example:

Read:
/docs/api.md

Update:
/src/routes.py

Create:
/reports/summary.md

This makes AI assistants feel much more like development partners.

3. PostgreSQL MCP Server

One limitation of traditional chatbots is that they don't know your data.

Connecting an MCP server to PostgreSQL changes that.

Now an AI can:

Execute SQL queries
Inspect schemas
Summarize tables
Generate reports
Help debug queries

Example request:

Find the top 10 customers by revenue in the last quarter.

Generate the SQL query and explain the result.

Instead of manually exporting data, the AI interacts directly with the database through a controlled interface.

4. Slack MCP Server

Many engineering teams live inside Slack.

Project updates.

Bug reports.

Deployment notifications.

Design discussions.

Imagine asking:

Summarize everything discussed in the #backend channel today.

Or:

List all unresolved deployment issues mentioned this week.

Instead of searching hundreds of messages, AI becomes an intelligent workspace assistant.

For distributed teams, this is incredibly valuable.

5. Browser MCP Server

Sometimes AI needs access to the web.

Not just search results.

Actual interaction.

A Browser MCP server allows AI systems to:

Open websites
Extract structured information
Fill forms
Navigate dashboards
Monitor pages

For example:

Open our staging dashboard.
Check whether all services are healthy.
Generate a status report.

This transforms AI from a conversational assistant into an operational assistant.

Why MCP Matters

When people talk about AI, they often focus on the language model.

I think the real value increasingly comes from what the model can do.

Without external tools, an LLM is limited to generating text.

With MCP, it can:

Read repositories
Query databases
Manage files
Access APIs
Communicate with enterprise systems

The model becomes part of a larger workflow rather than an isolated chatbot.

MCP Doesn't Replace Good Architecture

One lesson I've learned is that adding more tools doesn't automatically create a better AI system.

A poorly designed workflow connected to ten MCP servers is still a poorly designed workflow.

The goal isn't to maximize integrations.

The goal is to solve problems with the simplest architecture possible.

That's one reason I previously argued that many AI agents are overengineered.

Sometimes a well-designed workflow connected to a few MCP servers is far more effective than a complex multi-agent architecture.

Build the Foundation First

If you're just starting with AI development, don't try to learn every framework at once.

Begin by understanding:

How LLMs work
How prompts provide context
How vector databases enable retrieval
How APIs connect systems
How MCP standardizes tool access

Everything else builds on these foundations.

If you're looking for more AI frameworks and open-source tools worth exploring, I recently shared my favorite GitHub repositories for AI builders:

7 GitHub Repositories I Recommend to Every AI Builder

Several of those projects pair naturally with MCP-based workflows.

Final Thoughts

I believe Model Context Protocol is one of the most important developments in the AI ecosystem.

Not because it makes language models smarter.

But because it makes them more useful.

As AI moves beyond chat interfaces into real-world applications, standardized communication with external tools will become increasingly important.

The future of AI isn't just better models.

It's better connections between models and the systems we already use every day.

And for me, MCP has become one of the most practical steps toward that future.

The Real Reason Prompt Engineering Isn't Going Away

Jaideep Parashar — Thu, 25 Jun 2026 03:07:17 +0000

Every few months, I see another post declaring:

"Prompt engineering is dead."

Usually, the argument goes something like this:

AI models are getting smarter.
They understand natural language better.
You no longer need carefully crafted prompts.

On the surface, that sounds reasonable.

But after building AI workflows and experimenting with modern frameworks, I think the opposite is happening.

Prompt engineering isn't disappearing. It's evolving.

And if you're building AI applications, not just chatting with AI, you'll probably rely on it more than ever.

Prompt Engineering Was Never About Fancy Prompts

One of the biggest misconceptions is that prompt engineering is about writing magical sentences that somehow unlock hidden AI capabilities.

It isn't.

Good prompt engineering is about giving an AI system exactly what it needs to complete a task reliably.

Consider these two examples.

Poor prompt:

Write Python code.

Better prompt:

Write a Python FastAPI endpoint that accepts a CSV upload.
Requirements:

Use Python 3.12

Validate file type

Handle exceptions

Return JSON responses

Include comments explaining each step

The second prompt isn't "clever."

It's simply clearer.

And clarity scales.

AI Models Are Better, But They Still Need Context

Modern LLMs have become incredibly capable.

They can:

Generate code
Explain algorithms
Debug applications
Write tests
Refactor functions

But they still don't know:

Your architecture
Your coding standards
Your API contracts
Your deployment strategy
Your business requirements

That information comes from you.

And the way you provide it matters.

Prompt engineering is fundamentally the practice of supplying useful context.

Every AI Framework Depends on Good Prompts

Take a look at the most popular AI frameworks.

Whether you're using:

LangChain
LangGraph
CrewAI
LlamaIndex

Every one of them eventually sends prompts to an LLM.

Even sophisticated agent systems are built from sequences of prompts.

Agents don't eliminate prompt engineering.

They multiply it.

Instead of designing one prompt, you're now designing prompts for:

Planning
Tool selection
Retrieval
Reflection
Code generation
Error recovery

Prompt quality becomes even more important.

If you're exploring these frameworks, I recently shared a list of GitHub repositories that I think every AI builder should know. Several of them demonstrate how central prompt design still is:

7 GitHub Repositories I Recommend to Every AI Builder

Better Prompts Produce Better Code

Here's a practical example.

Instead of asking:

Build a REST API.

Try:

Build a REST API using FastAPI.
Requirements:

CRUD operations

SQLite database

SQLAlchemy ORM

Pydantic models

Dockerfile

Unit tests with pytest

OpenAPI documentation

The difference is dramatic.

You're reducing ambiguity.

That's what prompt engineering really does.

Prompt Engineering Is Becoming Context Engineering

This is where I think the industry is heading.

Today's AI applications rarely rely on prompts alone.

They combine:

System prompts
Retrieved documents
Conversation history
Tool outputs
API responses
User preferences
Memory

For example:

System Prompt
↓
Retrieved Documents
↓
User Request
↓
Tool Results
↓
Conversation Memory
↓
LLM Response

The prompt is now only one part of a larger context.

Many people are calling this context engineering, and I think that's a useful way to describe the shift.

The skill isn't disappearing.

It's expanding.

The Bigger Challenge Is Consistency

One-off prompts are easy.

Building reliable AI systems is hard.

Imagine you're generating production-ready code.

Would you rather use this?

Write a Python function.

Or this?

Generate production-ready Python code.

Requirements:

Type hints

Google-style docstrings

Exception handling

Logging

Unit tests

PEP 8 compliance

No deprecated libraries

The second prompt creates predictable outputs.

Consistency is what matters in production systems.

Prompt Engineering Is Really Interface Design

I don't think prompt engineering is just about talking to AI.

I think it's about designing the interface between humans and intelligent systems.

Good prompts define:

Expectations
Constraints
Objectives
Quality standards
Success criteria

In other words, they reduce uncertainty.

That remains valuable regardless of how powerful AI models become.

My Perspective

Every major improvement in AI has reduced the effort required to write prompts.

But every improvement has also increased the complexity of the systems we build.

We're no longer asking AI to generate a single function.

We're asking it to:

Write code
Call tools
Search documentation
Query vector databases
Execute workflows
Collaborate with other agents

Ironically, as AI systems become more capable, clear instructions become even more important.

That's one reason I recently argued that many AI agents are overengineered. Before introducing multiple agents, it's often worth improving the prompts and workflow first.

Final Thoughts

Prompt engineering isn't going away.

It's simply becoming more sophisticated.

Tomorrow's AI builders won't compete based on who writes the cleverest prompt.

They'll compete based on who designs the clearest systems.

Whether we call it prompt engineering, context engineering, or instruction design doesn't matter much.

The underlying skill remains the same:

Helping AI understand exactly what we want it to do.

And I believe that skill will remain one of the foundations of building reliable AI applications.

Why I Think Most AI Agents Are Overengineered

Jaideep Parashar — Mon, 22 Jun 2026 04:48:31 +0000

AI agents are everywhere.

Multi-agent systems.

Agent swarms.

Autonomous teams.

Planning agents.

Self-improving agents.

It seems every week a new framework appears promising to build the next generation of autonomous AI systems.

After spending considerable time studying and experimenting with AI workflows, I have come to a simple conclusion:

I think most AI agents are overengineered.

That doesn't mean agents are useless.

Far from it.

I simply believe many builders are solving problems with agents that could be solved with something much simpler.

The Industry Loves Complexity

Let's imagine you want to build a system that:

Reads PDFs.
Extracts information.
Stores embeddings.
Answers questions.

I've seen builders create architectures like this:

Research Agent
↓
Planner Agent
↓
Retriever Agent
↓
Memory Agent
↓
Answer Agent
↓
Reviewer Agent

Six agents.

Multiple prompts.

Complex state management.

Retries.

Memory synchronization.

And a lot of headaches.

Meanwhile, the same problem can often be solved with:

PDF → Chunk → Embed → Vector DB → LLM → Response

Sometimes a workflow is enough.

Not everything needs an agent army.

Workflows Solve Most Problems

In my experience, most AI applications are deterministic.

They follow a sequence:

Input
↓
Transform
↓
Retrieve
↓
Generate
↓
Output

Examples include:

Document Q&A
Customer support
Meeting summaries
Blog generation
Code review
Knowledge assistants

These are workflows.

Not autonomous systems.

And workflows are:

Easier to debug
Easier to scale
Easier to maintain
Easier to explain

Complexity should be earned, not assumed.

Agents Introduce Hidden Costs

Every additional agent brings:

More prompts

Which means more tokens.

More latency

Each step adds execution time.

More hallucination opportunities

One bad output propagates downstream.

More debugging pain

Finding failures becomes difficult.

More infrastructure complexity

Memory, orchestration, retries, and monitoring become necessary.

What started as a simple application suddenly becomes an engineering project.

Most Builders Don't Need Multi-Agent Systems

Let's compare.

Simple Workflow
documents → embeddings → Chroma → GPT → answer

Simple.

Reliable.

Fast.

Now compare that to:

Planner Agent
↓
Retriever Agent
↓
Research Agent
↓
Critic Agent
↓
Memory Agent
↓
Final Writer Agent

Do you really need six agents to answer questions from a PDF?

Probably not.

Where Agents Actually Shine

I'm not anti-agent.

I think agents are powerful when:

Long-running tasks exist

For example:

Researching across multiple websites
Monitoring APIs
Scheduling actions
Autonomous coding loops

Decision-making is required

For example:

if bug_found:
    fix_code()
elif tests_fail:
    rerun()
else:
    deploy()

Human intervention matters

Human-in-the-loop systems benefit greatly from agent architectures.

Multiple tools must collaborate

Email.

GitHub.

Slack.

Databases.

Web search.

This is where agents become interesting.

I Believe Workflows Matter More Than Agents

One thing I've learned is that builders often jump directly into agent frameworks.

CrewAI.

LangGraph.

AutoGen.

And many others.

But before building agents, I think we should first ask:

Can a workflow solve this?

If the answer is yes, start there.

Only introduce agents when complexity demands them.

Not because Twitter says agents are the future.

In fact, I recently shared some of my favorite repositories in:

"7 GitHub Repositories I Recommend to Every AI Builder"

Some of those tools are incredibly powerful—but power doesn't always mean more complexity.

Sometimes the best architecture is the simplest one.

The Software Industry Has Seen This Before

Microservices.

Kubernetes.

Distributed systems.

Event-driven architectures.

Many teams adopted them before they truly needed them.

AI may be repeating the same pattern.

Builders see impressive demos and assume every project needs:

Agent memory
Multi-agent orchestration
Planning loops
Reflection agents

But complexity isn't innovation.

Complexity is cost.

My Rule

I follow a simple principle:

Workflow first.
Agent second.
Multi-agent last.

Start with the simplest architecture possible.

Only add complexity when reality demands it.

Not because hype demands it.

Final Thoughts

AI agents are exciting.

Frameworks like LangGraph and CrewAI are pushing the ecosystem forward.

And I believe autonomous systems will play a major role in the future.

But today, I think many AI builders are overengineering solutions.

Most problems don't require a team of agents.

Most problems require clear workflows.

Because at the end of the day, users don't care whether your application has twelve agents.

They care that it works.

And in engineering, simplicity is often the most underrated feature.