12 MCP, RAG, and Agents cheat sheets covering:- Function calling & MCP for LLMs- 4 stages of training LLMs from scratch- Training LLMs using other LLMs- Supervised & Reinforcement fine-tuning- RAG vs Agentic RAG, and more.Check the detailed thread below 👇 https://t.co/erWhHLhldqAvi Chawla (@_avichawla):12 MCP, RAG, and Agents cheat sheets for AI engineers (with visuals): ...

That's a wrap!If you found it insightful, reshare it with your network.Find me → @_avichawlaEvery day, I share tutorials and insights on DS, ML, LLMs, and RAGs.Avi Chawla (@_avichawla):12 MCP, RAG, and Agents cheat sheets for AI engineers (with visuals): ...

1️⃣2️⃣ KV cachingKV caching is a technique used to speed up LLM inference.I have linked my detailed thread below👇 https://t.co/Dt1uH4iniqAvi Chawla (@_avichawla):KV caching in LLMs, clearly explained (with visuals): ...

12 MCP, RAG, and Agents cheat sheets for AI engineers (with visuals): ...

RT Avi Chawla (@_avichawla)Evaluate conversational LLM apps like ChatGPT in 3 steps (open-source).Unlike single-turn tasks, conversations unfold over multiple messages.This means that the LLM's behavior must be consistent, compliant, and context-aware across turns, not just accurate in one-shot output.In DeepEval, you can do that with just 3 steps:1) Define your multi-turn test case as a ConversationalTestCase.2) Define a metric with ConversationalGEval in plain English.3) Run the evaluation.Done!This will ...

If you found it insightful, reshare it with your network.Find me → @_avichawlaEvery day, I share tutorials and insights on DS, ML, LLMs, and RAGs.Avi Chawla (@_avichawla):Evaluate conversational LLM apps like ChatGPT in 3 steps (open-source).Unlike single-turn tasks, conversations unfold over multiple messages.This means that the LLM's behavior must be consistent, compliant, and context-aware across turns, not just accurate in one-shot output. https://t.co/dugCyqQl6D ...

GitHub repo: https://t.co/LfM6AdsO74(don't forget to star it ⭐) ...

Evaluate conversational LLM apps like ChatGPT in 3 steps (open-source).Unlike single-turn tasks, conversations unfold over multiple messages.This means that the LLM's behavior must be consistent, compliant, and context-aware across turns, not just accurate in one-shot output.In DeepEval, you can do that with just 3 steps:1) Define your multi-turn test case as a ConversationalTestCase.2) Define a metric with ConversationalGEval in plain English.3) Run the evaluation.Done!This will provide a detailed breakdow ...

I built a RAG system that queries 36M+ vectors in <0.03 seconds.The technique used makes RAG 32x memory efficient!Check the detailed breakdown with code below: https://t.co/5AYmUa2hJUAvi Chawla (@_avichawla):A simple technique makes RAG ~32x memory efficient!- Perplexity uses it in its search index- Azure uses it in its search pipeline- HubSpot uses it in its AI assistantLet's understand how to use it in RAG systems (with code): ...

That's a wrap!If you found it insightful, reshare it with your network.Find me → @_avichawlaEvery day, I share tutorials and insights on DS, ML, LLMs, and RAGs.Avi Chawla (@_avichawla):A simple technique makes RAG ~32x memory efficient!- Perplexity uses it in its search index- Azure uses it in its search pipeline- HubSpot uses it in its AI assistantLet's understand how to use it in RAG systems (with code): ...