I have been fine-tuning LLMs for more that 2 years now! Here are the top 5 LLM fine-tuning techniques, explained with visuals:

What is context engineering❓ And why is everyone talking about it...👇 Context engineering is rapidly becoming a crucial skill for AI engineers. It's no longer just about clever prompting; it's about the systematic orchestration of context. 🔷 The Problem: Most AI agents fail not because the models are bad, but because they lack the right context to succeed. Think about it: LLMs aren't mind readers. They can only work with what you give them. Context engineering involves creating dynamic systems that offer: - The right information - The right tools - In the right format This ensures the LLM can effectively complete the task. 🔶 Why Traditional Prompt Engineering not enough: Early on, we focused on "magic words" to coax better responses. But as AI applications grow complex, complete and structured context matters far more than clever phrasing. 🔷 4 Key Components of a Context Engineering System: 1️⃣ Dynamic Information Flow Context comes from multiple sources: users, previous interactions, external data, tool calls. Your system needs to pull it all together intelligently. 2️⃣ Smart Tool Access If your AI needs external information or actions, give it the right tools. Format the outputs so they're maximally digestible. 3️⃣ Memory Management - Short-term: Summarize long conversations - Long-term: Remember user preferences across sessions 4️⃣ Format Optimization A short, descriptive error message beats a massive JSON blob every time. 🔷 The Bottom Line Context engineering is becoming the new core skill because it addresses the real bottleneck: not model capability, but information architecture. As models get better, context quality becomes the limiting factor. I'll share more as things evolve and become more concrete! Stay tuned!! 🙌 ____ If you found it insightful, reshare with your network. Find me → @akshay_pachaar ✔️ For more insights and tutorials on LLMs, AI Agents, and Machine Learning!

Knowledge‑distillation in LLMs, clearly explained:

How LLMs train LLMs, clearly explained (with visuals):

Let's build a "Chat with your Code" RAG app using Qwen3-Coder:

MCP powered multimodal RAG app! It lets you do RAG over: - Audio - Video - Images - And text 100% open-source, here's a step-by-step guide:

I just built the ultimate MCP server for Multimodal AI. It lets you do RAG over audio, video, images and text! 100% open-source, here's the full breakdown...👇

Transformer vs. Mixture of Experts in LLMs, visually explained:

Universal Tool Calling Protocol! A safer, reliable, and scalable alternative to MCP. Universal Tool Calling Protocol (UTCP) lets any agent talk to any tool—HTTP, gRPC, WebSocket, even your local CLI—without wrappers, latency, or lock‑in. 100% open-source.

🤖 🛡️ Cleanlab Trust Scoring Cleanlab's powerful trust scoring system prevents AI hallucinations in customer support, seamlessly integrating with LangGraph to detect and block problematic responses before reaching users. Explore the technical implementation here: