DEV Community: Shreyans Padmani

LLM Integration Developer: What to Look For and Where to Find One

Shreyans Padmani — Fri, 17 Jul 2026 10:24:16 +0000

OpenAI's API was processing over 100 billion tokens a day by late 2024. McKinsey's 2025 State of AI report found 65 percent of organizations now use generative AI in at least one business function, up from 33 percent two years earlier. The bottleneck is no longer access to LLM APIs. It's finding a developer who can connect those APIs to real business workflows without the system hallucinating, ballooning in cost, or collapsing under production load.

An LLM integration developer is a specific kind of generative AI professional: someone who moves beyond prompt experimentation to build RAG pipelines, fine-tune models on proprietary data, design multi-agent orchestration, and ship production-grade systems with latency budgets and cost controls. The market has no shortage of people who can call an OpenAI API and return a completion. It has a genuine shortage of developers who can do that reliably, cheaply, and at scale.

Gartner's 2025 analysis found that 40 percent of enterprise LLM pilots fail to reach production within 18 months, primarily due to reliability, cost, and hallucination issues that were never addressed in development. The developer you hire either solves those problems proactively or leaves you to discover them after launch.

RAG Pipeline Design

What to Look For: Vector DB selection (Pinecone, pgvector), chunking strategy, retrieval tuning
Red Flag: Treats RAG as plug-and-play; cannot discuss retrieval quality or optimization

Fine-Tuning

What to Look For: LoRA, QLoRA, PEFT methods, dataset curation, real evaluation metrics
Red Flag: Claims fine-tuning experience but cannot name or explain an evaluation method

Production Deployment

What to Look For: FastAPI/LangServe, streaming responses, caching, latency optimization
Red Flag: Portfolio consists only of notebooks; no experience deploying live API endpoints

Cost Optimization

What to Look For: Token batching, model tiering (GPT-4o vs. mini models), semantic caching
Red Flag: No awareness of inference costs or inability to estimate costs for a specific use case

Why a specialist freelancer outperforms a big agency here
LLM systems are sensitive to context. Behavior depends on prompt design, retrieval configuration, and fine-tuning decisions made by whoever understands the business requirements. When an agency rotates a different engineer onto the project mid-build, that context is lost and gets rebuilt at the client's expense. A specialist freelancer with a verified track record owns the full project from discovery to deployment instead.

A senior LLM engineer at a US agency runs $180 to $280 an hour after overhead. A top-rated generative AI freelancer with equivalent production depth runs $50 to $90 an hour. On a 400-hour engagement, that's a $36,000 to $72,000 swing with no measurable quality trade-off.
For sourcing, Upwork's Expert Vetted and Top Rated tiers remain the strongest starting point: the job success score and public work history create accountability that anonymous job boards don't. For IP-sensitive projects, a personal referral from another technical founder or CTO is worth more than any platform badge.
The LLM APIs available in 2026 are capable of powering genuinely transformative systems. Most integration projects underdeliver not because the technology is insufficient, but because the developer hired to integrate it lacked the production engineering depth to turn API access into something reliable. The checklist and code above aren't a wish list. They're the minimum bar for anyone building something your business will depend on.
This piece was originally published in longer form on shreyans.tech, where it includes the full sourcing-channel comparison, five interview questions that reveal real LLM experience, and an FAQ section.

About the author: Shreyans Padmani is a freelance AI and generative AI developer with a 100 percent Upwork job success score, a Microsoft AI certification, and 12 published case studies with quantified business outcomes. He writes about production LLM engineering at shreyans.tech. If you're scoping an LLM integration, his generative AI development services page covers RAG pipelines, fine-tuning, and multi-agent orchestration across hourly, monthly, and fixed-price engagement models.

Data First VS AI First

Shreyans Padmani — Wed, 15 Jul 2026 03:15:46 +0000

Every business jumping into AI faces the same fork in the road: do you build your AI First strategy and figure out data later, or do you get your Data First foundations right and let the AI layer come after?

I've shipped 12 production AI systems across healthcare, finance, e-commerce, and manufacturing — and the pattern is consistent. Teams that go AI First almost always end up rebuilding. Teams that go Data First ship once, and ship right.

The AI First Trap

AI First teams start with the exciting part — picking a model, wiring up an LLM, building an agent — before asking whether the underlying data can even support it. This looks fast in week one and falls apart by week four, when:

The model's predictions don't match reality because training data was messy or biased
Nobody can explain why the AI made a decision (no data lineage)
Scaling breaks because the pipeline was never designed to handle production volume

I wrote more about this shift in my blog on AI Predictions 2026: from general AI models to vertical LLMs and autonomous agents — the industry is moving away from "bolt AI onto anything" toward domain-specific, data-grounded systems.

Why Data First Wins

Data First means you invest in data quality, structure, and governance before touching a model. It's less glamorous, but it's the difference between a demo and a deployed system.

A good real-world example: in my case study on AI Resume Screening for Recruiters, the win wasn't the NLP model itself — it was building custom entity extraction trained on the client's own hiring criteria instead of a generic matching engine. Data-first thinking cut review time by 70%.

Same story in NLP Customer Feedback Classification — real-time categorization across 12 sentiment dimensions only worked because the data pipeline was solid before the model was built.

I go deeper into this in How ML Consulting Transforms Data into Smarter Business Decisions — worth a read if you're trying to convince your team to slow down and fix the data layer first.

So Which One Should You Choose?

AI First → Best for quick prototypes and proof-of-concepts. Risk: breaks at scale, low trust in outputs.

Data First → Best for production systems and long-term ROI. Risk: slower initial setup.

My take: AI is only as good as the data feeding it. Garbage in, garbage out isn't a cliché — it's the #1 reason AI projects fail in production.

Conclusion

Data First and AI First aren't really competing philosophies — they're a sequencing choice, and the sequence matters more than the tools you pick. A model is only as reliable, explainable, and scalable as the data underneath it. Teams that respect that foundation ship systems that hold up in production; teams that skip it end up rebuilding what they already built.

If you want to see how data-first thinking translates into real production work, take a look here: AI Case Studies

Official Website: shreyans.tech

Human Vision VS Computer vision

Shreyans Padmani — Sat, 11 Jul 2026 02:43:06 +0000

Vision feels effortless to us — we open our eyes and instantly understand a busy street, a friend's face, or a moving car. But teaching a machine to "see" the same way is one of the hardest problems in AI. Let's break down how Human Vision and Computer Vision actually compare, and where they meet in real-world applications.

1. Human Vision — The Original Perception System

Definition

Human vision is a biological process where the eyes capture light, the retina converts it into electrical signals, and the brain interprets those signals into meaningful understanding — depth, motion, emotion, and context, all at once.

How It Works

Light enters the eye, hits the retina, and gets converted into neural signals sent through the optic nerve to the visual cortex. The brain doesn't just "see" pixels — it fills in gaps, recognizes patterns instantly, and connects what it sees with memory, emotion, and prior experience.

● Processes an entire scene in milliseconds

● Understands context, emotion, and intent instinctively

● Adapts instantly to new environments and lighting

● Learns from a handful of examples, not millions

● Cannot be copied, scaled, or run in parallel across systems

Think of human vision as a lifetime of training compressed into a system that never needs a dataset — it just understands.

2. Computer Vision — Teaching Machines to See

Definition

Computer Vision is a field of AI that enables machines to interpret and process visual data — images and video — using models trained on massive labeled datasets, so systems can detect, classify, and understand what's inside a frame.

How It Works

An image is broken down into pixels, and a trained model (typically a convolutional neural network or a modern vision transformer) scans those pixels for patterns — edges, shapes, textures — layer by layer, until it can confidently label objects, detect defects, or track movement.

Actual View

Two related deep-dives if you want to see this applied to real industries: Computer Vision in Manufacturing and Computer Vision for Inventory Management Systems

3. Human Vision vs Computer Vision — The Key Differences

	Human Vision	Computer Vision
Learning	Learns from few examples	Needs large labeled datasets
Speed at scale	Limited to one scene at a time	Processes thousands of frames per second
Consistency	Affected by fatigue, bias, mood	Consistent, tireless, repeatable
Context understanding	Deep, intuitive, emotional	Improving, but still literal
Scalability	Cannot be duplicated	Deployable across unlimited cameras/devices

Human vision wins on judgment and context. Computer Vision wins on scale, consistency, and speed.

Process

Image Classification

Image classification is a computer vision technique that assigns a single label to an image by analyzing visual features using trained models to accurately predict the correct category or class.

Object Localization

Object Localization is the task of locating an instance of a particular object category in an image, typically by specifying a tightly cropped bounding box centered on the instance.

Object detection

Object detection is a computer vision technique used to identify and locate objects in images or videos, enabling counting, tracking precise positions, and accurately labeling items within a scene..

Image Segmentation

Image segmentation involves converting an image into a collection of regions of pixels that are represented by a mask or a labeled image.
By dividing an image into segments, you can process only the important segments of the image instead of processing the entire image.

Use Case

One example of this last case is a real project where AR let customers visualize designs directly in their space in real time: AR Tile Visualization Case Study

For a look at how these vision systems get planned and shipped end-to-end, this breakdown of the full delivery process is useful: End-to-End AI Development Process

Conclusion

Computer Vision isn't trying to replicate human vision perfectly — it's trying to extend it. Where human eyes get tired, distracted, or simply can't be in a thousand places at once, machine vision picks up the slack, while human judgment still decides what actually matters. As these systems keep improving, understanding this partnership — not competition — is what separates AI hype from AI that actually ships.

If you want to see how vision-based AI systems translate into real production work, take a look here: AI Case Studies

Official Website: shreyans.tech

GraphRAG with Python: Smarter AI with Context

Shreyans Padmani — Thu, 09 Jul 2026 16:49:14 +0000

Traditional RAG (Retrieval-Augmented Generation) has one big blind spot: it retrieves chunks of text based on similarity, not relationships. That works fine for simple lookup questions, but it falls apart the moment a query needs information connected across multiple documents.

GraphRAG fixes this by combining knowledge graphs with RAG — giving your AI system actual context, not just nearby text.

1. The Problem with Traditional RAG

Traditional RAG pipelines work like this:

Split documents into chunks
Convert chunks into vector embeddings
Retrieve the "closest" chunks to a query
Feed them to the LLM

Where it breaks down

It can't connect facts that live in different chunks or documents
It misses multi-hop reasoning (e.g., "Who manages the team that built Project X?")
It struggles with holistic, summary-style questions across a large corpus I go deeper into how standard RAG works and where it's genuinely useful here: RAG in Generative AI: Dynamic Information Access

2. What Is GraphRAG?

GraphRAG (Graph-based Retrieval-Augmented Generation) replaces flat vector search with a knowledge graph — a structure of entities (nodes) and relationships (edges) extracted from your data.

What GraphRAG adds

Entity extraction — pulling out people, systems, concepts, and events from raw text
Relationship mapping — connecting entities based on how they interact
Graph traversal — following relationships to answer multi-hop questions
Community summarization — clustering related entities to answer broad, high-level questions Think of traditional RAG as searching a filing cabinet, and GraphRAG as reading a mind map that already understands how everything connects.

3. How It Works? (GraphRAG with Python)

At a high level, a GraphRAG pipeline flows through four steps:

1. User Query
The user asks a question to the AI system.

2. Retrieve Data
The system performs a vector search to fetch relevant documents or information.

3. Map Relationships (Graph)
A knowledge graph identifies connected entities and relationships for deeper context.

4. Generate AI Answer
The LLM combines the retrieved data with the graph context to generate an accurate, grounded response.

This is the core idea behind GraphRAG: it doesn't replace vector search — it adds a relationship layer on top of it, so the LLM isn't just working with "nearby" text, but with connected context.

4. GraphRAG With Python

GraphRAG combines vector retrieval and knowledge graphs to provide structured, relationship-aware context for large language models generating accurate answers.
Python enables building GraphRAG pipelines using embeddings, graph databases, and LLM frameworks for intelligent multi-hop reasoning systems.
GraphRAG reduces hallucinations by grounding responses in connected entities and relationships instead of isolated document chunks only.

Why Python for GraphRAG?
Python offers powerful libraries for AI, machine learning, and natural language processing tasks.
A rich ecosystem supports embeddings, vector databases, and knowledge graph integrations seamlessly.
Easy integration with LLM frameworks like LangChain and OpenAI APIs.
Simple syntax enables faster development of complex GraphRAG pipelines efficiently.
For a primer on how LLMs and Generative AI relate to each other before diving into GraphRAG specifics, see: LLM vs Generative AI: Understanding the Core Differences

6. GraphRAG vs Traditional RAG

Aspect	Traditional RAG	GraphRAG
Retrieval basis	Vector similarity	Entity relationships + graph traversal
Multi-hop reasoning	Weak	Strong
Corpus-wide summaries	Poor	Strong (via community detection)
Setup complexity	Low	Higher
Compute cost	Lower	Higher (LLM calls during indexing)
Best for	Direct lookup questions	Connected, relational, and summary questions

7. Where GraphRAG Actually Pays Off

GraphRAG isn't always the right tool — it adds real indexing cost and complexity. It earns its keep when:

Your data has rich relationships (org charts, supply chains, medical records, codebases)
Queries require connecting facts across multiple documents
You need explainable answers that show why the AI reached a conclusion, not just what it retrieved For a broader look at how vertical, context-aware LLM systems like this are shaping real production AI in 2026, I wrote about it here: AI Predictions 2026: From General AI Models to Vertical LLMs and Autonomous Agents

And if you're exploring how this fits into a broader AI Agent or automation pipeline (agents calling into a GraphRAG layer for grounded, relationship-aware decisions), that's something I build custom here: AI Agent Development

Conclusion

Traditional RAG gives an LLM access to knowledge. GraphRAG gives it understanding of how that knowledge connects. By combining knowledge graphs with retrieval, GraphRAG unlocks multi-hop reasoning, holistic summaries, and explainable answers that flat vector search simply can't provide.

If you're building an AI system that needs to reason across connected data — not just fetch the nearest chunk — GraphRAG is worth the extra setup.

If you're curious how this plays out in real production projects, check out these real-world builds:

🔗 Official Website: https://shreyans.tech/

Understanding Prompting Techniques in AI

Shreyans Padmani — Tue, 07 Jul 2026 03:00:55 +0000

Large language models are only as good as the prompts you give them. The same model can look mediocre or brilliant depending on how you ask it to do something. Below is a practical rundown of the eight core prompting techniques every developer working with LLMs should know — from the simplest zero-shot ask to full reasoning-plus-tool-use agents.

1. Zero-Shot Prompting

You ask the model to do a task with no examples at all — just a clear instruction. It leans entirely on what it learned during pretraining to produce a response.

When to use it: simple, well-known tasks (summarizing, translating, basic Q&A) where the model doesn't need to see the exact output format first.

2. One-Shot Prompting

You give the model exactly one example of the input/output pattern you want before asking your real question. That single demonstration is often enough to anchor the format and tone of the response.

When to use it: when the task is simple but the format matters — e.g., you want output as a specific JSON shape or a particular writing style.

3. Few-Shot Prompting

Same idea as one-shot, but with several (2–5) examples. More examples mean the model can pick up on patterns rather than guessing from a single case, which improves both accuracy and consistency.

Interestingly, research on in-context learning has found that what matters most isn't necessarily whether every example's label is perfectly correct — the diversity of inputs and the overall structure of the examples matter a great deal too. A well-known study by Min et al. (2022) found that even examples with randomly shuffled labels still outperformed zero-shot prompting.

When to use it: classification, structured extraction, or any task where you want the model to mimic a precise style or schema.

If you want to see few-shot and NLP-style prompting applied in a real project, I broke down how it worked for automatic feedback categorization here: NLP Customer Feedback Classification

4. Chain-of-Thought (CoT) Prompting

Instead of asking for a final answer directly, you ask the model to reason step by step. This breaks a complex problem into smaller logical steps, which tends to produce more accurate results on math, logic, and multi-step reasoning problems.

A neat shortcut discovered in research: you don't always need worked examples to get this benefit. Kojima et al. showed that simply appending "let's think step by step" to a prompt can trigger step-by-step reasoning even with zero examples. Note: this trick mainly helps standard models — reasoning models (extended-thinking, o-series, etc.) already do this internally.

5. Self-Consistency Prompting

The model generates multiple independent reasoning paths for the same question (usually via sampling), and you select the most common or most consistent final answer among them. It's "ensemble voting" applied to reasoning chains.

6. Role-Based Prompting

You assign the model a persona — teacher, senior developer, security auditor, etc. — before giving it the task. This nudges vocabulary, depth, and structure to match that role's expertise and tone.

7. Instruction Tuning / Task-Specific Prompting

This is about being explicit and structured: clearly defined task requirements, precise instructions, and a well-organized prompt format. The tighter and more structured your instructions, the more precisely the model matches your intent.

8. ReAct (Reasoning + Acting) Prompting

ReAct combines reasoning with real actions — the model thinks step by step and can call external tools or fetch external data (API calls, search, database queries) while forming its answer. This is the pattern behind most modern AI agents.

Which Technique Should You Actually Use?

Most production prompts aren't "pure" versions of any one technique — they're combinations.

Key takeaways:

Few-shot prompting remains one of the best return-on-effort techniques for structured or stylistic tasks.
Zero-shot CoT ("think step by step") is a nearly free upgrade for reasoning tasks on standard models.
Self-consistency is a good lever when reliability matters more than latency or cost.
ReAct-style prompting is what you reach for once your model needs to interact with the outside world.

Prompt engineering is still evolving fast — Tree-of-Thought and Graph-of-Thought are pushing past linear reasoning into branching, mergeable reasoning paths for enterprise-grade decisions. But these eight techniques remain the foundation.

Where this is all heading — from general-purpose LLMs to vertical, domain-specific models and autonomous agents — is something I cover in more depth here: AI Predictions 2026: From General AI Models to Vertical LLMs and Autonomous Agents

If you're curious how these prompting techniques hold up in real production projects, check out more real-world builds here: AI Case Studies

Conclusion

Prompting isn't a single skill — it's a toolbox. Zero-shot and one-shot get you quick answers, few-shot and role-based prompting shape tone and format, chain-of-thought and self-consistency sharpen reasoning, and ReAct lets the model reach beyond its own knowledge into the real world. Knowing which lever to pull, and when, is what separates a mediocre AI output from a genuinely useful one.

🔗 Official Website: shreyans.tech

Understanding Modern AI Architecture: LLMs, RAG, AI Agents & MCP

Shreyans Padmani — Sun, 05 Jul 2026 15:29:27 +0000

If you've been building with AI lately, you've probably noticed that "just call the LLM" doesn't cut it anymore. Real-world AI systems today are built from four core pieces that work together like a nervous system — each one solving a different limitation of the last. Let's break them down one by one.

1. LLM (Large Language Model) — The Brain

LLMs are the core reasoning engines of modern AI systems.

What they do well

Understand and generate human-like text
Reason based on patterns learned during training
Write code, summarize content, and explain complex concepts

What they don't do

They don't know your internal or private documents
They don't retain long-term memory across sessions
They don't take real-world actions on their own

Think of an LLM as a brilliant brain — intelligent, but isolated — with no access to your files and no ability to act.

2. RAG (Retrieval-Augmented Generation) — Brain + Knowledge

RAG extends LLMs by connecting them to trusted external knowledge sources.

What RAG adds

Retrieval from PDFs, databases, APIs, and internal systems
Embeddings and vector search to find relevant information
Up-to-date, verifiable, and context-aware answers

Example

Query: "What is our company's refund policy?"

Flow: RAG retrieves the policy document → the LLM explains it clearly.

If you want to see RAG applied in a real industry, I broke down how it's used in finance here: Generative AI in Fintech: Use Cases, Benefits, and Real-World Examples

3. AI Agents — Brain + Hands

AI Agents go beyond answering questions. They are designed to take action.

What AI Agents can do

Use tools and APIs
Make decisions at runtime
Execute multi-step workflows
Track state, progress, and context

Example Workflow

Read an incoming email
Extract key details
Update a CRM system
Send a response
Notify a user or team

I've written a full breakdown of how agents work in a real domain here: AI Agents in Healthcare: Transforming Modern Medicine

Since agents can act on their own, security matters a lot — here's a practical framework for keeping agents safe: Practical AI Agent Security

4. MCP (Model Context Protocol) — The Nervous System

MCP is the connective layer that ties everything together.

What MCP does

Standardizes how LLMs interact with tools and services
Enables secure, structured communication
Makes AI systems modular, reusable, and scalable

MCP allows

Agents to communicate with tools reliably
RAG pipelines to fetch data safely
LLMs to operate in real production environments

Putting It All Together

Component	Role	Analogy
LLM	Reasoning engine	Brain
RAG	Knowledge retrieval	Brain + Knowledge
AI Agents	Action & decision-making	Brain + Hands
MCP	Standardized connectivity	Nervous System

Modern AI applications rarely rely on just one of these. A production-grade system usually stacks all four: an LLM for reasoning, RAG to ground it in real data, Agents to let it act, and MCP to let everything talk to each other safely and reliably.

Most AI projects don't fail because of the tech itself — they fail due to poor alignment between business goals and the AI solution. I cover this in detail here: The Silent Threat to AI Initiatives

Conclusion

AI systems today are no longer just "one big model doing everything." They're layered architectures — each piece compensating for what the other lacks. The LLM brings reasoning and language understanding, RAG grounds it in real, trustworthy data, AI Agents turn that reasoning into real-world action, and MCP ties it all together with a reliable, standardized way for these components to communicate.

As AI applications move from simple chatbots to autonomous systems that read, decide, and act on their own, understanding this architecture isn't optional anymore — it's the foundation.

If you're curious how this plays out in real production projects, check out these real-world builds: AI Case Studies

🔗 Official Website: https://shreyans.tech/

What part of this stack are you working with right now — RAG pipelines, agent workflows, or MCP integrations? Let me know in the comments!

How PageIndex Rethinks RAG Without Vector Search

Shreyans Padmani — Wed, 03 Jun 2026 17:34:21 +0000

It started with a simple frustration: a RAG system that was fast, scalable, and technically correct — yet still gave answers that felt slightly off. The data was there, embeddings were generated, and vector search was working as expected, but the results often resembled “almost right” instead of precise. Like flipping through a book and landing on pages that are related but not exactly what you need. This is the limitation of traditional vector-based retrieval — it relies on similarity, not certainty. PageIndex challenges this idea by replacing semantic guessing with structured navigation, allowing systems to retrieve exactly the right information, not just the closest match.

What is PageIndex?

PageIndex is an alternative approach to traditional Retrieval-Augmented Generation (RAG) that removes the dependency on vector embeddings and similarity search. Instead of converting text into vectors and storing them in a vector database, PageIndex organizes information in a structured, hierarchical, and page-based format.

ai-agent-development-freelancer

Think of it like a smart book index.

Instead of searching by “semantic similarity” (as in vector search), PageIndex allows the system to navigate directly to the most relevant sections of data using structured references, metadata, and logical grouping.

This makes it:

More deterministic (less guesswork than vector similarity)
Easier to debug and trace
Often more cost-efficient (no embedding or vector DB overhead)

How PageIndex Works

1. Data Organization into Pages

Instead of chunking text arbitrarily, data is divided into logical pages.
Each page represents a coherent unit of information — like a section, topic, or document fragment.

For example:

Page 1 → “Introduction to RAG”
Page 2 → “Vector Databases Explained”
Page 3 → “AWS Architecture”
This preserves meaning better than random chunking.

2. Metadata & Index Creation

Each page is enriched with metadata such as:

Titles
Keywords
Tags
Relationships to other pages
An index is then created — similar to how a search engine or book index works.

This allows fast lookup without needing embeddings.

3. Query Understanding

When a user asks a question, the system analyzes the query using an LLM (such as GPT-4 or Claude).

Instead of converting the query into a vector, it:

Extracts intent
Identifies key topics
Maps the query to relevant indexed pages

4. Page Retrieval (No Vector Search)

Using the index, the system directly retrieves relevant pages.

This can involve:

Keyword matching
Metadata filtering
Hierarchical navigation
Because it’s structured, retrieval is:

Faster
More predictable
Easier to control

5. Context Assembly
The retrieved pages are combined into a structured context.

Unlike traditional RAG (which may return loosely related chunks), PageIndex ensures:

Coherent information flow
Logical grouping
Minimal redundancy

6. Response Generation

Finally, the selected pages are passed to an LLM to generate the answer.

Since the context is cleaner and more relevant, the model:

Produces more accurate responses
Reduces hallucinations
Maintains better consistency

Conclusion

PageIndex rethinks RAG by removing vector search and using structured, page-based retrieval for more precise and explainable results. It reduces reliance on semantic similarity, improves accuracy, and offers a simpler, more deterministic way to access relevant knowledge in AI systems.

Naive RAG vs Agentic RAG: The Evolution of Intelligent Retrieval

Shreyans Padmani — Tue, 02 Jun 2026 16:21:21 +0000

Naive RAG retrieves relevant documents and generates answers from them in a single step. Agentic RAG goes further by planning, reasoning, validating information, and performing multiple retrieval cycles when needed. This makes Agentic RAG more accurate, adaptable, and effective for solving complex real-world problems.

rag-in-generative-ai-dynamic-information-access

Naive Retrieval (Naive RAG)

How It Works

User gives a query
Example: “Affordable eco-friendly smartphones under $500.”

Query is converted into embeddings
The system transforms text into vector format.

Similarity Search (Top-K Retrieval)
It searches the vector database and retrieves the closest matching documents.

Chunk Injection (Augmentation)
Retrieved content is directly inserted into the prompt.

LLM Generates Response
The model produces an answer based only on those retrieved chunks.

Limitations
No planning or strategy
No filtering by structured conditions (price, rating, etc.)
No validation or re-ranking
Depends fully on first retrieval
Can produce incomplete or “mid” responses

Agentic Retrieval (Agentic RAG)

How It Works

User Query → Task Understanding
The agent analyzes the intent behind the question.

Deconstruct & Plan
It breaks the request into smaller sub-tasks.
Example:
Filter by price < $500
Filter eco-friendly category
Check customer ratings

Multi-Collection Search
Searches across multiple vector databases if needed.

Aggregate & Re-Rank
Combines results and ranks them based on relevance and constraints.

Observe & Repeat
If results are not sufficient, it refines the search again.

Synthesize Final Answer
Generates a structured, accurate, high-quality response.

Advantages

Strategic planning
Iterative refinement
Better reasoning
More accurate filtering
Higher quality outputs
Handles complex queries better

Conclusion

Naive RAG retrieves information and generates answers. Agentic RAG goes several steps further — it plans, searches intelligently, refines its approach, and synthesizes knowledge before responding.

As AI applications continue to evolve, Agentic RAG is emerging as a key architecture for building more reliable, accurate, and intelligent enterprise AI systems.

official-Website — shreyans.tech/

Building Blocks of RAG on AWS: From Data to Intelligence

Shreyans Padmani — Sun, 31 May 2026 13:37:18 +0000

In the era of generative AI, simply asking a model a question is no longer enough. The real value lies in grounding responses in your own data — documents, databases, and knowledge bases that matter to your business. This is where Retrieval-Augmented Generation (RAG) comes in. For a deeper dive into how RAG enables dynamic access to information, check out my detailed blog here:

Rag-in-gererative-ai-dynamic-information-access

RAG is not just a buzzword; it’s a practical architecture that combines information retrieval with generative AI to produce accurate, context-aware answers. And when it comes to deploying RAG at scale, AWS offers a powerful set of building blocks.

Let’s walk through the story of how a RAG system comes together on AWS.

Data Processing Pipeline: Turning Raw Data into Intelligence
At the heart of any RAG system lies a quiet but critical layer — the data processing pipeline. This is where raw, unstructured information is transformed into something a machine can actually understand, search, and reason over.

Think of it as the backstage crew of a theater production. The audience never sees it, but without it, the show simply wouldn’t work.

A) Storage — The Source of Truth
Every pipeline begins with storage.

Your original documents — PDFs, Word files, logs, HTML pages — are typically stored in Amazon S3. It acts as a durable, scalable data lake where all raw inputs reside.

The key advantage here is simplicity and flexibility:

Store any file type
Scale virtually without limits
Integrate easily with downstream AWS services
At this stage, your data is complete — but not yet usable for semantic search.

B) Chunking — Making Data Digestible
Large documents are split into smaller, meaningful chunks using services like AWS Lambda, Amazon ECS, or Apache Airflow.
Effective chunking ensures semantic continuity, adds slight overlap for context, and preserves metadata — all of which directly impact retrieval quality.

C) Embedding API — Converting Text into Meaning
Chunked text is converted into vector embeddings using Amazon Bedrock (e.g., Titan models).
This transforms text into numerical representations where similar meanings are closer in vector space, enabling semantic search instead of keyword matching.

D) Vector Store — Building Searchable Memory
Embeddings are stored in Amazon OpenSearch Service for fast similarity search.
It allows scalable storage, quick retrieval, and management of millions of vectors along with metadata.

E) Vector Search — Finding What Matters
User queries are embedded and matched against stored vectors in Amazon OpenSearch Service to retrieve the most relevant chunks.
Accurate results depend on clean data, effective chunking, and high-quality embeddings.

Generic RAG Architecture

Data Processing Pipeline (Generic RAG)
A) Storage — Centralized Data Foundation
Store raw documents in scalable systems such as cloud storage, databases, or document repositories. This layer acts as the single source of truth for all downstream processing.

B) Chunking — Structuring Unstructured Data
Pre-process and split documents into semantically meaningful chunks using tools like LangChain or LlamaIndex.
Well-designed chunking preserves context, introduces overlap, and significantly improves retrieval accuracy.

C) Embedding API — Semantic Transformation
Convert text chunks into dense vector embeddings using advanced embedding models.
This step encodes meaning into numerical space, enabling similarity-based retrieval beyond keyword matching.

D) Vector Store — Efficient Knowledge Indexing
Store and index embeddings in vector databases such as Pinecone, Weaviate, FAISS, or Chroma.
These systems enable fast, scalable, and low-latency search over large datasets.

E) Vector Search — Context Retrieval Engine
Perform semantic search using similarity metrics like cosine similarity or dot product to fetch the most relevant chunks.
This ensures the model receives precise, context-rich information for response generation.

F) Prompt Engineering — Guiding the Model
Design structured prompts that combine user queries with retrieved context.
Clear instructions and formatting help reduce hallucinations and improve response reliability.

G) LLM Selection — Choosing the Right Model
Select an appropriate LLM such as GPT-4, Claude, Llama 3, or Mistral based on performance, cost, latency, and use case requirements.

H) API Layer — System Integration
Expose the RAG pipeline via APIs built with frameworks like FastAPI or Flask.
This layer connects your backend intelligence to frontend applications, enabling real-time interaction.

Conclusion

Retrieval-Augmented Generation (RAG) is more than just connecting a model to data — it’s about designing a pipeline that turns raw information into reliable intelligence. From structured storage and smart chunking to embeddings, vector search, and LLM-powered responses, each building block plays a critical role in the system’s accuracy and performance.

A well-architected RAG system doesn’t just generate answers — it delivers context-aware, trustworthy insights grounded in real data. As organizations continue to adopt AI, mastering these building blocks will be key to building scalable, efficient, and production-ready applications.

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Different Between of MVC controller and APIController

Shreyans Padmani — Sun, 08 Mar 2026 11:21:42 +0000

In ASP.NET Core, both MVC Controllers and API Controllers handle HTTP requests, but they serve different purposes and are used in different application architectures. (Link — https://shreyans.tech/computer-vision-development-freelancer)

MVC Controller(ASP.Net MVC)

1️⃣ Handles HTTP Requests
Receives incoming browser requests and decides how to respond.

2️⃣ Returns Views (HTML)
Primarily used to return Razor Views (.cshtml) to render UI pages.

3️⃣ Inherits from Controller Class
Provides built-in support for views, model binding, TempData, and ViewBag.

4️⃣ Part of MVC Pattern
Acts as the middle layer between Model (data/business logic) and View (UI).

5️⃣ Supports Action Methods
Public methods inside the controller are called action methods and respond to routes.

6️⃣ Model Binding Support
Automatically binds form data, query strings, and route values to model objects.

7️⃣ ViewData, ViewBag & TempData
Used to pass data from Controller to View.

8️⃣ Supports Filters
Allows use of Action Filters, Authorization Filters, and Exception Filters.

9️⃣ Routing Integration
Works with ASP.NET routing to map URLs to specific controller actions.

🔟 Best for Server-Rendered Applications
Ideal for traditional web applications where the server generates HTML pages.

API Controller(ASP.Net Web API)

1️⃣ Designed for RESTful APIs
Handles HTTP requests and returns data (JSON, XML) instead of HTML views.

2️⃣ Inherits from ControllerBase
Lightweight base class optimized for API scenarios without view support.

3️⃣ Uses [ApiController] Attribute
Enables automatic model validation, binding, and better API conventions.

4️⃣ Returns Action Results or Data Objects
Common return types include IActionResult, ActionResult, or raw objects serialized to JSON/XML.

5️⃣ Supports Routing via Attributes
Uses [Route("api/[controller]")] and [HttpGet], [HttpPost] for endpoint mapping.

6️⃣ Automatic Model Binding & Validation
Request bodies are automatically bound to models, and invalid models trigger HTTP 400 responses.

7️⃣ Supports HTTP Methods Explicitly
Action methods can handle GET, POST, PUT, DELETE, PATCH using attributes.

8️⃣ Ideal for Frontend & Mobile Integration
APIs are consumed by Angular, React, mobile apps, or other services.

9️⃣ Stateless by Nature
API Controllers generally do not maintain session or state, following REST principles.

🔟 Supports Filters & Middleware
Can use authentication, authorization, exception handling, and logging filters for API behavior.

Conclusion

In ASP.NET, MVC Controllers and API Controllers serve different purposes. MVC Controllers are designed to render UI views for traditional web applications, while API Controllers are optimized for data-centric RESTful APIs, returning JSON or XML.

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How to build Multi-Tenant app with EF Core

Shreyans Padmani — Sun, 08 Mar 2026 11:14:00 +0000

Build a multi-tenant app with EF Core by adding TenantId to entities, applying global query filters, resolving tenant per request, isolating data via shared or separate databases, and enforcing secure dependency injection configuration.

EF Query Filters

Global Filtering
Automatically apply conditions to all queries for an entity (e.g., soft delete, multi-tenancy).

Defined in OnModelCreating
Configured using HasQueryFilter() inside the DbContext model builder.

Soft Delete Support
Commonly used to filter out records where IsDeleted = true.

Multi-Tenant Isolation
Filters data by TenantId to ensure users access only their tenant’s data.

Automatically Applied
No need to manually add Where clauses in every query.

Can Be Disabled Temporarily
Use IgnoreQueryFilters() when you need to bypass global filters.

**Supports Parameters
**Filters can use context-level variables (e.g., current tenant or user ID).

Improves Security & Consistency
Reduces risk of accidental data exposure and keeps filtering logic centralized.

Example

EF + Dynamic Connection String Resolution

Tenant-Based Database Selection
Dynamically choose a connection string based on the current tenant.

Supports Multi-Tenant Architecture
Enables separate databases per tenant for stronger data isolation.

Resolve Per Request
Determine the connection string from request data (subdomain, header, JWT claim).

Use DbContextOptionsBuilder
Configure the connection string inside OnConfiguring() or during service registration.

Dependency Injection Friendly
Inject a TenantProvider or ConnectionStringResolver service into DbContext.

Improves Security & Scalability
Isolates tenant data and allows independent scaling or migration.

Works with Multiple Providers
Can dynamically switch between SQL Server, PostgreSQL, etc., if required.

Supports Central Configuration Store
Store tenant connection strings in a master database or configuration service.

Requires Careful Lifetime Management
DbContext should be scoped per request to avoid cross-tenant leakage.

Useful for SaaS Applications
Ideal for enterprise SaaS platforms needing strict data separation.

Example

Conclusion

Dynamic connection string resolution in EF Core is a powerful approach for building scalable and secure multi-tenant applications. By resolving the database connection per request, you can ensure proper tenant isolation, improve data security, and enable flexible scaling strategies. When combined with dependency injection and proper DbContext scoping, it becomes a clean and maintainable solution for modern SaaS architectures.

In short, EF Core with dynamic connection string resolution enables true database-level multi-tenancy with flexibility and control.

Why We Use LangChain? — To Smoothly Connect with LLMs

Shreyans Padmani — Sun, 08 Mar 2026 11:05:07 +0000

In today’s AI-driven world, Large Language Models (LLMs) like OpenAI’s GPT models, Google’s Gemini, and Anthropic’s Claude are powerful tools. However, using them effectively in real-world applications requires more than just sending prompts and receiving responses.

What is LangChain ?

LangChain is an open-source Python framework for building application powered by Large Language Models (LLM)

Makes LLM context-aware by integrating external data and tools

Commonly Used for:

Chatbots
Question Answering
Document Analysis
RAG (Retrieval-Augmented Generation)

Why LangChain ?

Out-of-the-box LLM support (OpenAI, Hugging Face, Anthropic, etc.)
APIs
Vector databases (Pinecone, FAISS, Chroma)
Document loaders (PDF, CSV, web scraping)
Supports prompt templates, chains, and agents

How LangChain Works with LLMs

LangChain helps LLMs understand context, remember past interactions, and connect multiple steps to handle complex tasks easily.

Prompt Templates: Define reusable structures for questions or instructions, ensuring consistent and clear communication with the LLM.

Chains: Connect multiple steps or model calls to perform complex reasoning or multi-stage tasks automatically.

Memory: Allows the model to remember previous inputs and responses, giving conversations a continuous and contextual flow.

Agents: Enable the model to decide which action or tool to use next, such as searching data or calling an API.

Conclusion

In a world where Large Language Models (LLMs) are becoming central to modern applications, simply calling an API is no longer enough. We need structure, memory, workflows, and seamless integration with external tools and data sources. LangChain provides that missing layer.

By enabling prompt management, memory handling, Retrieval-Augmented Generation (RAG), and tool integration, LangChain transforms raw LLM capabilities into scalable, production-ready AI systems. Whether you’re building chatbots, AI agents, or data-driven assistants, LangChain helps bridge the gap between powerful models and practical real-world solutions.

In short, we use LangChain to smoothly connect with LLMs — and to turn intelligence into impact.

DEV Community: Shreyans Padmani

LLM Integration Developer: What to Look For and Where to Find One

Data First VS AI First

The AI First Trap

Why Data First Wins

So Which One Should You Choose?

Conclusion

Human Vision VS Computer vision

1. Human Vision — The Original Perception System

2. Computer Vision — Teaching Machines to See

3. Human Vision vs Computer Vision — The Key Differences

Process

Use Case

Conclusion

GraphRAG with Python: Smarter AI with Context

1. The Problem with Traditional RAG

2. What Is GraphRAG?

What GraphRAG adds

3. How It Works? (GraphRAG with Python)

4. GraphRAG With Python

Why Python for GraphRAG?

6. GraphRAG vs Traditional RAG

7. Where GraphRAG Actually Pays Off

Conclusion

Understanding Prompting Techniques in AI

1. Zero-Shot Prompting

2. One-Shot Prompting

3. Few-Shot Prompting

4. Chain-of-Thought (CoT) Prompting

5. Self-Consistency Prompting

6. Role-Based Prompting

7. Instruction Tuning / Task-Specific Prompting

8. ReAct (Reasoning + Acting) Prompting

Which Technique Should You Actually Use?

Conclusion

Understanding Modern AI Architecture: LLMs, RAG, AI Agents & MCP

1. LLM (Large Language Model) — The Brain

2. RAG (Retrieval-Augmented Generation) — Brain + Knowledge

3. AI Agents — Brain + Hands

4. MCP (Model Context Protocol) — The Nervous System

Putting It All Together

Conclusion

How PageIndex Rethinks RAG Without Vector Search

What is PageIndex?

How PageIndex Works

Conclusion

Naive RAG vs Agentic RAG: The Evolution of Intelligent Retrieval

Naive Retrieval (Naive RAG)

How It Works

Agentic Retrieval (Agentic RAG)

Conclusion

Building Blocks of RAG on AWS: From Data to Intelligence

Generic RAG Architecture

Conclusion

Different Between of MVC controller and APIController

MVC Controller(ASP.Net MVC)

API Controller(ASP.Net Web API)

Conclusion

How to build Multi-Tenant app with EF Core

EF Query Filters

EF + Dynamic Connection String Resolution

Conclusion

Why We Use LangChain? — To Smoothly Connect with LLMs

What is LangChain ?

How LangChain Works with LLMs

Conclusion