10 MCP, AI Agents, and RAG projects for AI Engineers (with code):

You're in an ML Engineer interview at Netflix. The interviewer asks: "You’ve trained a new recommendation model. How do you make sure it’s ready to replace the old one?" You reply: "I’ll compare metrics on validation and test sets." Interview over. Here’s what you missed:

Google did it again! First, they launched ADK, a fully open-source framework to build, orchestrate, evaluate, and deploy production-grade Agentic systems. And now, they have made it even powerful! Google ADK is now fully compatible with all three major AI protocols out there:

If you found it insightful, reshare it with your network. Find me → @_avichawla Every day, I share tutorials and insights on DS, ML, LLMs, and RAGs.

GitHub repo: https://t.co/FfVx9UU6d3 (don't forget to star it ⭐ )

Google did it again! First, they launched ADK, a fully open-source framework to build, orchestrate, evaluate, and deploy production-grade Agentic systems. And now, they have made it even powerful! Google ADK is now fully compatible with all three major AI protocols out there:

LLM inference speed with vs. without KV caching:

If you found it insightful, reshare it with your network. Find me → @_avichawla Every day, I share tutorials and insights on DS, ML, LLMs, and RAGs.

The visual explains the underlying details of KV caching. I also wrote a detailed explainer thread on KV caching a few months back, if you want to learn more. Check below👇

LLM inference speed with vs. without KV caching:

You're in an ML Engineer interview at Apple. The interviewer asks: "You have to build an ML-based face unlock system for iPhones. How would you train the model?" You: "I will capture user's images & train a binary classifier on them" Interview over. Here's what you missed:

You're in an ML Engineer interview at Apple. The interviewer asks: "You have to build an ML-based face unlock system for iPhones. How would you train the model?" You: "I will capture user's images & train a binary classifier on them" Interview over. Here's what you missed:

JSON prompting for LLMs, clearly explained:

That's a wrap! If you found it insightful, reshare it with your network. Find me → @_avichawla Every day, I share tutorials and insights on DS, ML, LLMs, and RAGs.

To summarise: Structured (JSON) prompting for LLMs is like writing modular code; it brings clarity of thought, makes adding new requirements effortless, & creates better communication with AI. It's not just a technique, but rather evolving towards a habit worth developing for

So, are json prompts the best option? Well, good alternatives exist! Many models excel at other formats: - Claude handles XML exceptionally well - Markdown provides structure without overhead 👉 So it's mainly about structure rather than syntax! Check this out👇

3️⃣ Reusable templates → Scalability, Speed & Clean handoffs You can turn JSON prompts into shareable templates for consistent outputs. Teams can plug results directly into APIs, databases, and apps; no manual formatting, so work stays reliable and moves much faster.

2️⃣ You control the outputs Prompting isn't just about what you ask; it's about what you expect back. Whether generating content, reports, or insights, JSON prompts ensure a consistent structure every time. No more surprises, just predictable results!

1️⃣ Structure means certainty JSON forces you to think in terms of fields and values, which is a gift. It eliminates gray areas and guesswork. Here's a simple example:

Why is JSON so effective? AI models are trained on massive amounts of structured data from APIs and web applications. When you speak their "native language," they respond with laser precision! Let's understand this a bit more...👇

The Problem with Natural Language Prompts Natural language is powerful yet vague! When you give instructions like "summarize this email" or "give me key takeaways," you leave room for interpretation, which can lead to hallucinations. And if you try JSON prompts: