DEV Community: Kenan Bharatbhai Gain

One-Stop Developer Guide to Prompt Engineering Across OpenAI, Anthropic, and Google

Kenan Bharatbhai Gain — Mon, 07 Jul 2025 22:41:17 +0000

One-Stop Developer Guide to Prompt Engineering Across OpenAI, Anthropic, and Google

As developers building with LLMs (Large Language Models), we’re not just writing code—we’re crafting conversations, instructions, and data-driven requests that machines can interpret and execute. That’s where prompt engineering comes in.

Whether you're integrating OpenAI's GPT-4, experimenting with Claude by Anthropic, or deploying apps using Google’s Gemini models—understanding how to write effective prompts is essential to building reliable, scalable AI systems.

Why Prompt Engineering Still Matters

While the long-term goal is natural and intuitive interactions with AI, today’s systems require well-structured, role-based, and context-aware prompting to reduce hallucinations, boost accuracy, and ensure trust.

Below is a comprehensive set of official resources released by OpenAI, Anthropic, and Google—ideal for developers at any level:

✅ OpenAI: ChatGPT 4.1 Prompt Engineering Guide

Official Guide: https://platform.openai.com/docs/guides/prompt-engineering

OpenAI’s guide focuses on formatting structure for ChatGPT and GPT-4.1, emphasizing the power of system messages and role assignment.

Key Developer Takeaways:

Use system messages to define behavior and tone
Structure multi-turn conversations using user and assistant roles
Improve performance with few-shot examples, step-by-step instructions, and function calling
Techniques to reduce drift, prevent hallucinations, and improve consistency in responses

✅ Anthropic: Claude Prompt Engineering Interactive Course

GitHub: https://github.com/anthropics/prompt-eng-interactive-tutorial

Google Drive: https://drive.google.com/drive/folders/148YBrLaqPdI-EUI7DMXIAJ9ErrWAUPv0

One of the most developer-friendly, hands-on tutorials for Claude AI. Created by Anthropic, it provides real-world prompt patterns across experience levels.

Course Modules:

Ch. 1–3: Direct prompting, clarity, role definition, formatting
Ch. 4–7: Use of examples, step-by-step thinking, output formatting
Ch. 8–9 + Appendix: Avoiding hallucinations, advanced reasoning, chaining prompts, API calls

Developer Highlights:

JSON-based prompt formatting
Domain-specific templates (legal, chat, finance, dev)
Best practices for safety, fairness, reliability

"That’s called prompt engineering. There’s an artistry to that."

— Jensen Huang, CEO of NVIDIA

✅ Google: Prompt Engineering Guide for Gemini

Official Guide: https://cloud.google.com/vertex-ai/docs/generative-ai/prompt-engineering/best-practices

Google’s guide is aimed at production-level AI deployments using Vertex AI and Gemini. Best suited for scalable, enterprise-grade solutions.

Key Developer Takeaways:

Few-shot vs zero-shot prompt comparison
Chain-of-thought prompting for intermediate reasoning
Grounding techniques to reduce hallucinations
Structured outputs (JSON, Markdown, Tables, etc.)
Support for multimodal prompting (text + image)

"Prompting is a new programming paradigm."

— Google DeepMind Prompt Engineering Team

🛠️ Use Case Snapshot

Platform	Key Strengths	When to Use
OpenAI	System message control, function calling, tooling	Assistants, copilots, APIs, RAG setups
Claude	Long-form reasoning, ethical frameworks	Regulated environments, chat, compliance
Google	Multimodal support, enterprise-ready deployment	Scalable apps, Vertex AI, production LLMs

🔧 Developer Applications

AI assistants and smart chatbots
Legal and financial document QA
Conversational APIs and RAG systems
Code generation and step-by-step explanations
Structured data generation for workflows
Secure and factual knowledge retrieval bots

#PromptEngineering

#AIForDevelopers

#OpenAI

#ClaudeAI

#GoogleAI

#LLMs

#GenAI

#Anthropic

#VertexAI

#Gemini

#GPT4

#KenanGain

#AIEngineering

#AIResources

#DeveloperTools

#AIProductDevelopment

50+ AWS Services Unleashed: Cloud Chaos Conquered with a Chuckle!

Kenan Bharatbhai Gain — Sun, 20 Apr 2025 23:25:42 +0000

Buckle up for a wild ride through AWS’s galaxy of services! From robot wranglers to quantum questers, here’s your snappy guide to 50+ AWS tools, complete with what they do, why they’re awesome, where they shine, and how to get started—plus links to dive deeper. Let’s tame the cloud beast!

1. AWS CloudFront Origin

Definition: The source (e.g., S3 bucket, EC2, HTTP server) that CloudFront fetches content from to cache and distribute globally.
Why We Use It: Zips content to users with low latency by stashing it at edge locations.
Use Cases: Static websites, media streaming, dynamic APIs.
Steps to Perform:
- Create an S3 bucket or set up an HTTP server (e.g., EC2).
- Launch a CloudFront distribution in the AWS Console.
- Point to the origin (S3 or server URL).
- Tweak cache settings, TTL, and security.
- Deploy and test the CloudFront URL.

2. AWS RoboMaker

Definition: A service for building, simulating, and deploying robotic apps using ROS (RoboMaker).
Why We Use It: Makes robot dev a breeze with simulation and fleet management.
Use Cases: Autonomous vehicles, warehouse bots, smart homes.
Steps to Perform:
- Set up a RoboMaker environment in the AWS Console.
- Code a ROS app using the provided IDE.
- Simulate in RoboMaker’s virtual world.
- Deploy to real robots or fleets.

3. AWS IoT Core

Definition: Connects IoT devices to the cloud for secure data exchange (IoT Core).
Why We Use It: Manages IoT fleets and integrates with AWS services.
Use Cases: Smart homes, industrial IoT, connected cars.
Steps to Perform:
- Register devices in IoT Core and issue certificates.
- Set secure MQTT policies.
- Pub/sub to topics for data flow.
- Hook up with Lambda or Kinesis for processing.

4. AWS Ground Station

Definition: Manages satellite comms and data processing (Ground Station).
Why We Use It: No need to own a satellite dish—AWS has you covered!
Use Cases: Weather data, Earth observation, telemetry.
Steps to Perform:
- Book a satellite contact window in the Console.
- Set up a mission profile (antenna, data flow).
- Pull in satellite data to S3 or other services.
- Analyze with tools like SageMaker.

5. Amazon Braket (Quantum Computing)

Definition: Access quantum computers and simulators for experiments (Braket).
Why We Use It: Dabbles in quantum algorithms for futuristic computing.
Use Cases: Optimization, cryptography, material sims.
Steps to Perform:
- Open Braket in the AWS Console.
- Code a quantum task with Python SDK.
- Pick a processor or simulator (e.g., D-Wave, IonQ).
- Run and check results in Jupyter or APIs.

For the remaining 47 AWS services, check out the full list on GitHub: https://github.com/KenanGain/50-AWS-Services

𝐁𝐞𝐬𝐭 𝐏𝐫𝐨𝐣𝐞𝐜𝐭 𝐒𝐡𝐨𝐰𝐜𝐚𝐬𝐞 𝐨𝐧 𝐋𝐢𝐧𝐤𝐞𝐝𝐈𝐧 EVER...

Kenan Bharatbhai Gain — Wed, 26 Feb 2025 19:35:40 +0000

Full Video link: https://www.linkedin.com/feed/update/urn:li:activity:7290621709374676994/

𝐁𝐞𝐬𝐭 𝐏𝐫𝐨𝐣𝐞𝐜𝐭 𝐒𝐡𝐨𝐰𝐜𝐚𝐬𝐞 𝐨𝐧 𝐋𝐢𝐧𝐤𝐞𝐝𝐈𝐍 EVER...

Broader Vision Behind This Project

Showcasing how I combine web development, big data, AI, cloud computing, and natural language processing to tackle real-world challenges and transform customer engagement. This project integrates cutting-edge technologies, including OSINT frameworks, to enhance data-driven decision-making and improve user experiences.

Project Document: https://lnkd.in/g4ybnc6S

Case Studies Included

Walmart

The pioneer of low-cost retailing & supply chain efficiency, leveraging AI and big data to optimize logistics and customer experience.
DMart - Avenue Supermarts Ltd

A highly profitable Indian retail chain, mastering cost-conscious operations and strategic real estate investments to ensure steady profits.
Canadian Tire Corporation

A uniquely Canadian legacy brand, balancing physical retail with digital transformation, incorporating AI-driven customer insights to enhance engagement.

𝐅𝐨𝐫 𝐈𝐧𝐪𝐮𝐢𝐫𝐢𝐞𝐬, 𝐏𝐚𝐫𝐭𝐧𝐞𝐫𝐬𝐡𝐢𝐩𝐬, 𝐚𝐧𝐝 𝐌𝐨𝐫𝐞

GitHub: https://lnkd.in/gwHKUZXK
LinkedIn: https://lnkd.in/gzTMXy2V
YouTube: https://lnkd.in/gDR-RiTf
Instagram: https://lnkd.in/gcag8fPU
Twitter: https://lnkd.in/gQPydJNT
Bento.me: https://bento.me/kenangain
Website: https://lnkd.in/gzxdpbuK
Discord: https://lnkd.in/gYmHwhPV
Email: kenangain2910@gmail.com

Leveraging Technologies From:

Vercel
Elastic
OpenAI
Google
Microsoft / Microsoft AI / Microsoft Azure
Docker, Inc
DeepSeek AI

𝐃𝐢𝐬𝐜𝐥𝐚𝐢𝐦𝐞𝐫

This project is created solely for non-profit and educational purposes, demonstrating how innovative technologies can address real-world challenges.

Movie Recommendations for Developers & Startup Founders

Kenan Bharatbhai Gain — Sun, 15 Sep 2024 23:41:24 +0000

1. The Hummingbird Project

"In the race for the fastest data, innovation is key."

A movie about the high-stakes world of high-frequency trading, showcasing how critical innovation, perseverance, and speed are in the modern tech world. For developers working on low-latency systems or startup founders in FinTech, this film illustrates the importance of cutting-edge solutions to stay ahead.

Lesson: Speed isn't everything, but it's close.

2. Blackberry

"Rise and fall of a tech empire."

The story of Blackberry is a prime example of how rapid technological advancements can outpace even the giants. This film offers key lessons about the importance of adaptability and staying relevant in the ever-changing tech landscape.

Lesson: Innovation is continuous—don't stop evolving.

3. Ready Player One

"Building the future in the virtual world."

For developers and founders interested in VR/AR, this film is a treasure trove of inspiration. It imagines a future where virtual reality is everything, pushing the boundaries of immersive technology. If you're working on projects that involve metaverse, game development, or immersive environments, this is a must-watch.

Lesson: The future is virtual, and it's already here.

4. Ironman 1-3

"Every developer dreams of being Tony Stark."

Tony Stark isn't just a superhero—he's the ultimate tech entrepreneur. From his first suit to fully automating the Stark industry, Ironman is a great reminder of how tech innovation can change the world. His story highlights the importance of relentless iteration, problem-solving, and vision.

Lesson: Build, test, iterate—innovation never stops.

5. The Dark Knight

"Leadership in the face of adversity."

Batman’s leadership amidst chaos offers startup founders a model for leading in tough times. It's a story of resilience, persistence, and the ability to pivot under pressure—key traits for anyone building a business from scratch.

Lesson: True leadership shines in crisis.

6. The Big Short

"Seeing the future before it happens."

This film serves as a crucial reminder to founders and developers about anticipating market trends and understanding financial risks. The characters in The Big Short spot the 2008 financial crisis before anyone else. As an innovator, you need to think ahead and understand the long-term implications of your decisions.

Lesson: Disrupt before you're disrupted.

7. Dumb Money

"When the little guy takes on the system."

This movie is about retail investors disrupting Wall Street, which can be directly correlated to how startups challenge established companies. For developers and founders, this shows that with the right product and a strong community, even the smallest player can make waves in the market.

Lesson: Innovation democratizes success.

8. Gran Turismo

"Turning a game into reality."

Gran Turismo showcases the incredible journey of turning a passion for video games into a career in racing. For developers, this is a reminder of how skills honed in virtual environments can translate to real-world achievements.

Lesson: Passion for technology can drive real-world success.

9. Tetris

"The classic puzzle of perseverance."

The story of Tetris is a reminder that simplicity can sometimes lead to global success. Developers and founders often chase the most complex solutions, but sometimes the simplest ideas can change the world.

Lesson: Keep it simple, and you'll go far.

10. The Imitation Game

"The birth of modern computing."

Alan Turing's journey in breaking the Enigma code is the foundation of modern computing. For developers, this is a powerful story of how persistence, problem-solving, and innovation can lead to breakthroughs.

Lesson: Greatness often comes from solving the hardest problems.

11. Moneyball

"Innovation in a traditional world."

Moneyball is a fantastic story for startup founders and developers about using data to disrupt traditional industries. By leveraging data analytics, the underdog Oakland A's team was able to compete against far bigger organizations.

Lesson: Data-driven decisions can beat intuition.

12. The Founder

"Scaling an empire."

The story of McDonald's rapid expansion offers invaluable lessons on scaling a startup. Founders can learn about the importance of systems, partnerships, and finding the right business model.

Lesson: Scaling requires structure and vision.

13. Steve Jobs (Jobs & Steve Jobs Films)

"Visionary, relentless, and flawed."

Both films focus on Steve Jobs, his successes, failures, and vision. Jobs' journey offers countless lessons for founders—from product design to leadership, from pushing boundaries to dealing with failure.

Lesson: Visionaries aren’t born; they're made through persistence.

14. The Pursuit of Happiness

"Overcoming the impossible."

This film is an inspiration to any entrepreneur who feels overwhelmed by the odds. Will Smith's portrayal of Chris Gardner shows that with perseverance, dedication, and grit, anyone can achieve their dreams, no matter how difficult the journey.

Lesson: Never give up, no matter how hard it gets.

The Social Network

"How one idea changed the world."

The Social Network tells the story of how Facebook started from a Harvard dorm room and grew into the tech giant it is today. The movie is a must-watch for startup founders and developers alike. It shows the raw side of entrepreneurship, how ideas are born, and the fierce challenges that come with scaling a business. The journey of Mark Zuckerberg highlights the importance of vision, strategy, and even the complexities of navigating partnerships.

Lesson: Ideas are just the beginning, execution is everything.

The Future of Local LLM Execution: Running Language Models Locally with Ollama, ONNX, and More

Kenan Bharatbhai Gain — Sat, 14 Sep 2024 18:30:02 +0000

In today’s rapidly evolving AI landscape, large language models (LLMs) have revolutionized how we interact with technology. From text generation to advanced chatbots, LLMs can assist in diverse applications like customer service, content creation, and more. Traditionally, most developers rely on cloud-based solutions such as OpenAI’s GPT or Google’s Bard. However, these come with drawbacks like high costs, latency, and concerns over data privacy.

Thankfully, new tools are making it easier to run LLMs locally, providing cost-effective, privacy-conscious alternatives without sacrificing performance. This post explores various local LLM execution platforms such as Ollama, ONNX, Wasm, GPT-J, and TensorFlow.js. We’ll dive into the pros and cons of each, explain how to set them up, and explore why local execution may be the future of LLM deployment.

Why Run LLMs Locally?

Running LLMs locally has several advantages:

Cost-Effectiveness: Avoid cloud infrastructure costs and the need for expensive APIs.
Data Privacy: All data processing happens on your machine, ensuring privacy.
Customization: Local models allow you to tweak and fine-tune based on your specific needs.
Latency Reduction: Running models locally reduces network delays.

Now, let’s dive into the top platforms for local execution and how to use them effectively.

1. Wasm (WebAssembly)

Wasm is a powerful technology that lets you run lightweight models entirely in the browser. This makes it perfect for applications that require client-side execution, such as basic NLP tasks without needing a backend.

Pros:

Cross-Platform: Works on any browser, so no installations are needed.
Client-Side Execution: Keeps everything local, ideal for privacy-sensitive applications.

Cons:

Limited Model Size: Can’t efficiently handle larger models like GPT-3 due to browser memory constraints.
Lower Performance: More suitable for smaller models like GPT-2 or distilled models.

How to Run:

To run a model using Wasm, simply embed the necessary WebAssembly code into your HTML and execute it client-side. Libraries such as Transformers.js can make this easier, as we’ll see later.

Best for: Lightweight models and basic tasks where no server infrastructure is required.

2. ONNX (Open Neural Network Exchange)

ONNX is a format for deep learning models that enables them to be executed locally, either on CPU or GPU. It supports a wide range of frameworks, including PyTorch and TensorFlow, and allows for hardware acceleration, making it one of the best tools for running larger, more complex models locally.

Pros:

Local Execution: Models are optimized for local hardware, offering performance benefits.
GPU Support: If you have the hardware, ONNX allows for GPU acceleration, which can drastically improve inference times.

Cons:

Complex Setup: Requires model conversion and optimization.
High Resource Usage: Larger models will need strong hardware.

How to Run:

Install ONNX runtime:

   pip install onnxruntime

Convert your model to ONNX format:

   import torch
   import onnx
   torch.onnx.export(your_model, dummy_input, "model.onnx")

Run the model locally:

   import onnxruntime as rt
   session = rt.InferenceSession("model.onnx")

Best for: Users with custom-trained models who need optimized performance on their local hardware.

3. GPT-J / GPT-Neo

OpenAI’s GPT-3 models are powerful but come with restrictions in terms of usage and control. GPT-J and GPT-Neo are open-source alternatives that can be run locally, giving you more flexibility without sacrificing performance.

Pros:

Open Source: Full control over the model and its setup.
High Quality: Competitive with GPT-3, providing powerful text generation capabilities.

Cons:

Hardware Requirements: Needs a high-performance GPU (8GB+ VRAM) to run efficiently.
Complex Setup: Requires a fair bit of technical expertise to run locally.

How to Run:

Install GPT-J:

   git clone https://github.com/kingoflolz/mesh-transformer-jax
   cd mesh-transformer-jax
   pip install -r requirements.txt

Fine-tune or run the model locally by setting up the environment and passing input data.

Best for: Running powerful, customizable models for local AI applications.

4. Transformers.js

Transformers.js allows you to run NLP models directly in the browser using JavaScript, leveraging the power of WebAssembly (Wasm). It’s ideal for browser-based applications that need LLM functionality without depending on a server.

Pros:

Runs in Browser: Everything stays client-side, which is great for privacy.
Popular Models Available: You can run pre-trained models like GPT-2, BERT, etc.

Cons:

Limited Model Size: Larger models like GPT-3 cannot run effectively in the browser.
Performance: Browser execution may be slower compared to server-side.

How to Run:

Install Transformers.js:

   <script src="https://cdn.jsdelivr.net/npm/@xenova/transformers"></script>

Load and run a model:

   const pipeline = await transformers.pipeline('text-generation');
   const output = await pipeline('Hello, I am a language model');

Best for: Small NLP tasks that need to run in-browser with no backend dependencies.

5. TensorFlow.js

TensorFlow.js is another powerful library for running machine learning models in the browser or in a Node.js environment. It supports both CPU and GPU execution, making it a flexible tool for web-based ML applications.

Pros:

**Runs i

`

{% embed  %}
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n Browser or Node.js**: Offers flexible deployment options.

Pre-Trained Models: Models like BERT are available for local execution.

Cons:

Limited by Model Size: Large models are constrained by browser memory.
Complexity: Requires models to be converted to TensorFlow.js format for browser use.

How to Run:

Convert your model to TensorFlow.js format:

   tensorflowjs_converter --input_format=tf_saved_model /path/to/saved_model /path/to/tfjs_model

Run in the browser:

   const model = await tf.loadGraphModel('path/to/model.json');
   const prediction = model.predict(tf.tensor(inputData));

Best for: Web-based applications requiring flexible deployment in both client and server environments.

6. Ollama (Local LLM Execution)

Ollama is a newcomer to the local LLM scene, offering a streamlined experience for running models like LLaMA and Mistral directly on your machine. It’s incredibly user-friendly and removes much of the complexity traditionally associated with running LLMs locally.

Pros:

Easy to Use: Simple setup with commands like ollama run and ollama list.
Local Execution: Runs entirely on your machine, which means zero cloud costs and complete control over data privacy.
Versatile Models: Supports various LLMs, including LLaMA and Mistral.

Cons:

Hardware Dependency: Running larger models will require a machine with sufficient memory and processing power.
Limited Scalability: While Ollama works great for small to medium projects, it lacks the scalability of cloud-based solutions for larger deployments.

How to Run:

Install Docker and pull Ollama’s Docker image:

   docker pull ollama/ollama

Run Ollama:

   docker run -it ollama/ollama

Run a specific model:

   ollama run <model_name>

Best for: Developers who want to run LLMs locally without complex setups, making it ideal for small to medium projects.

Conclusion: Which Local Execution Option Is Right for You?

If you’re just starting out or need a simple, user-friendly setup, Ollama is the perfect tool. For users needing more performance and hardware-accelerated execution, ONNX or GPT-J may be better choices. Meanwhile, browser-based options like Wasm and Transformers.js provide excellent lightweight alternatives for client-side applications.

Running LLMs locally is quickly becoming a practical, efficient alternative to cloud solutions, empowering developers with more control, lower costs, and greater privacy. Ready to dive in?

Github Readme Profile update with new social media cards stats

Kenan Bharatbhai Gain — Thu, 04 Jul 2024 15:04:37 +0000

github.com/KenanGain

Realtime AI gemini nano using vercel ai sdk

Kenan Bharatbhai Gain — Thu, 04 Jul 2024 02:17:45 +0000

Check out the original post on LinkedIn.