RAG Implementation - Enterprises are building RAG (Retrieval-Augmented Generation) systems over hundreds of data sources, not just one [1] - The industry is building MCP (Most Capable Platform)-powered RAG over 200+ sources, with 100% local data processing [1] Platform Adoption - Microsoft includes it in M365 products [1] - Google includes it in its Vertex AI Search [1] - AWS includes it in its Amazon Q Business [1]

AI Agent Development - The industry is focusing on building AI Agents in production [1] - The industry provides hands-on tutorials for learning AI Agent development [1]

Author & Content Focus - Avi Chawla 分享关于数据科学 (DS)、机器学习 (ML)、大型语言模型 (LLMs) 和检索增强生成 (RAGs) 的教程和见解 [1] - Avi Chawla 拥有一年多在生产环境中构建 AI Agents 的经验 [1] Call to Action - 鼓励读者分享报告，扩大影响力 [1] - 提供了一个关于构建 AI Agents 的简单教程 [1]

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]

Content Overview - The document is a wrap-up message from Avi Chawla (@_avichawla) encouraging readers to reshare the content if they found it insightful [1] - Avi Chawla shares tutorials and insights on Data Science (DS), Machine Learning (ML), Large Language Models (LLMs), and Retrieval-Augmented Generation (RAGs) daily [1] - Avi Chawla provides 12 cheat sheets for AI engineers related to MCP, RAG, and Agents, including visuals [1]

Core Technique - KV caching is a technique used to speed up LLM inference [1] Explanation Resource - Avi Chawla provides a clear explanation of KV caching in LLMs with visuals [1]

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]