RAG Implementation - Enterprises are building RAG (Retrieval-Augmented Generation) systems over hundreds of data sources [1] - The industry is moving towards RAG implementations across 200+ data sources, emphasizing local processing [1] MCP-Powered RAG Adoption - Microsoft includes MCP-powered RAG in M365 products [1] - Google integrates it into Vertex AI Search [1] - AWS offers it through Amazon Q Business [1]

RAG技术应用 - 企业正在构建基于超过 100 个数据源的 RAG 系统 [1] - Microsoft 在 M365 产品中提供 RAG 技术 [1] - Google 在 Vertex AI Search 中提供 RAG 技术 [1] - AWS 在 Amazon Q Business 中提供 RAG 技术 [1] 技术趋势 - 行业正在构建基于 MCP 驱动的 RAG 系统，数据源超过 200 个，并且 100% 本地化 [1]

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]

Core Insights - The performance of AI products increasingly relies on data quality and freshness rather than just model size [1][2][3] - Companies like Salesforce and IBM are acquiring data infrastructure firms to enhance their AI capabilities with real-time, structured data [2][5][6] - The definition of "good data" includes being domain-specific, continuously updated, structured, deduplicated, and real-time actionable [4][5][6] Data Infrastructure Importance - Data collection is now seen as a critical infrastructure rather than a secondary task, emphasizing the need for reliable, real-time access to data [2][9][22] - The modern AI data stack has evolved into a value chain that includes data acquisition, transformation, organization, and storage [10][22] - Effective data retrieval quality surpasses prompt engineering, as outdated or irrelevant data can hinder model performance [7][19] Strategic Data Collection - Data collection must be strategic, providing structured and immediate data for AI agents [12][13] - It should handle dynamic user interfaces, CAPTCHAs, and mixed extraction methods to ensure comprehensive data gathering [14][15] - Data collection infrastructure should be scalable and compliant with legal standards, moving beyond fragile scraping tools [16][22] Future of AI Systems - The future of AI performance will depend more on knowledge acquisition speed and context management rather than just model size [23][24] - Companies that view data collection as a foundational capability will likely achieve faster and more cost-effective success [25]

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): ...

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): ...

RT Avi Chawla (@_avichawla)You can now deploy any ML model, RAG, or Agent as an MCP server.And it takes just 10 lines of code.Here's a step-by-step breakdown (100% private): ...

RAG技术栈 - RAG技术栈范围从最简单的内存嵌入和相关性排序搜索，到最复杂的行星级搜索，后者包含70多种语料库混合，包括token、embeddings和知识图谱[1] - 行业正在探索在200毫秒内以每秒16万次查询的速度，对这些混合语料库进行联合检索、自定义排序、联合重排序和LLM处理[1] - 报告通过“一次一个查询”的方式，逐步增加复杂性，展示RAG中所有技术的局限性，以及下一层技术在处理更复杂查询方面的能力[1] 搜索挑战 - 某些搜索问题非常难以解决，以至于行业可能更倾向于将问题交给LLM或UX处理[1] - 报告指出，像[falafel]这样的查询非常难以搜索，而对文档进行分块可能会是灾难性的[1] 行业应用与洞察 - Google团队在50多个搜索产品（包括Google.com和定制企业搜索）的背景下，分享了RAG技术的应用经验[1] - Pi Labs 致力于将Google在搜索核心AI和NLU系统方面的工作经验带给整个行业[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):You can now deploy any ML model, RAG, or Agent as an MCP server.And it takes just 10 lines of code.Here's a step-by-step breakdown (100% private): ...