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):I have been building AI Agents in production for over an year.If you want to learn too, here's a simple tutorial (hands-on): ...

That's how you can build any production-grade Agent and even connect it to Slack in a few steps.You can find more details at Product Hunt: https://t.co/ceJPWyQOdh(don't forget to upvote ⬆️ ) ...

I have been building AI Agents in production for over an year.If you want to learn too, here's a simple tutorial (hands-on): ...

AI Engineering Resources - The document provides 12 cheat sheets for AI engineers covering various topics [1] - The cheat sheets include visuals to aid understanding [1] Key AI Topics Covered - Function calling & MCP (likely Mean Cumulative Probability) for LLMs (Large Language Models) is covered [1] - The cheat sheets detail 4 stages of training LLMs from scratch [1] - Training LLMs using other LLMs is explained [1] - Supervised & Reinforcement fine-tuning techniques are included [1] - RAG (Retrieval-Augmented Generation) vs Agentic RAG is differentiated [1]

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): ...

Conversational LLM Evaluation - DeepEval enables evaluation of conversational LLM applications like ChatGPT in three steps [1] - Unlike single-turn tasks, conversational LLMs require consistent, compliant, and context-aware behavior across multiple messages [1] DeepEval Features - DeepEval allows defining multi-turn test cases as ConversationalTestCase [1] - DeepEval allows defining metrics with ConversationalGEval in plain English [1] - DeepEval provides a detailed breakdown of conversation success/failure and a score distribution [2] - DeepEval offers a full UI to inspect individual turns [2] Open-Source Aspects - DeepEval is 100% open-source with approximately 10 thousand stars [2] - DeepEval can be self-hosted, ensuring data privacy [2]

LLM Evaluation - The industry is focusing on evaluating conversational LLM applications like ChatGPT in a multi-turn context [1] - Unlike single-turn tasks, conversations require LLMs to maintain consistency, compliance, and context-awareness across multiple messages [1] Key Considerations - LLM behavior should be consistent, compliant, and context-aware across turns, not just accurate in one-shot output [1]

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