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transformation, organization, and storage [10][22] - Effective data retrieval quality surpasses prompt engineering, as outdated or irrelevant data can hinder model performance [7][19] Strategic Data Collection - Data collection must be strategic, providing structured and immediate data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]

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]

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]

Technology & Development - ML 模型、RAG 或 Agent 现在可以部署为 MCP 服务器 [1] - 仅需 10 行代码即可完成部署 [1] - 提供逐步分解说明 (100% private/完全私有) [1]

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): ...

Core Viewpoint - The article emphasizes the importance of integrating existing programming skills with large model technologies to enhance career prospects in the AI field, rather than abandoning current skills [1]. Summary by Sections Course Overview - A course titled "Large Model Application Development Practical Training" is designed to help developers master AI application development from scratch through practical projects and code breakdowns [1]. - The course includes insights from industry experts and real case studies from major companies, providing participants with high-paying job opportunities and internal referrals [1][15]. Course Content - The curriculum covers essential concepts such as RAG (Retrieval-Augmented Generation), AI Agent, and Transformer architecture, focusing on practical applications and fine-tuning techniques [9][11]. - It consists of five modules: basics, tools, advanced topics, competitions, and practical applications, ensuring a comprehensive learning path [9]. Target Audience - The course is aimed at developers looking to connect with product teams, build technical barriers, avoid job insecurity, and enhance their skills for future career development [13]. - It is particularly relevant for programmers concerned about job stability as they age, especially those nearing the 35-year mark [13]. Success Metrics - The course has successfully served over 20,000 students, receiving positive feedback and helping many secure high-paying job offers [11]. - Participants learn to customize models for specific industries such as manufacturing, healthcare, and finance, improving task accuracy and efficiency [11]. Practical Experience - The course includes detailed case studies of popular AI applications, allowing participants to gain hands-on experience and build a portfolio of practical projects [16]. - Students will learn to implement AI technologies in various business scenarios, enhancing their employability [16]. Career Development - The course offers insights into current job market trends for large model technologies, including salary expectations and career growth opportunities [20]. - Continuous internal referral opportunities are provided, ensuring participants have a direct pathway to high-paying positions in leading companies [20].

Core Viewpoint - The article emphasizes the importance of integrating existing programming skills with large model technologies to enhance career prospects and salary opportunities in the AI field [1]. Group 1: Course Offerings - A course titled "Large Model Application Development Practical Training" is designed to help developers master the complete AI application development process through practical projects and code breakdown [1]. - The course covers essential technologies such as RAG, AI Agent, and Transformer architecture, providing a comprehensive learning path from basics to advanced applications [8]. - The course has served over 20,000 students and has received positive feedback, with many participants securing high-paying job offers [10]. Group 2: Learning Outcomes - Participants will learn to fine-tune mainstream large models like DeepSeek and Qwen for specific scenarios, improving model performance and task accuracy [10]. - The course includes practical applications of RAG technology for efficient knowledge retrieval and generation in various sectors such as law, healthcare, and finance [10]. - Students will also learn to design and develop AI Agents for multi-task collaboration and complex problem-solving in industry-specific contexts [10]. Group 3: Career Development - The course aims to help participants build technical barriers, avoid job insecurity, and enhance their career development over the next 20 years [12]. - It offers insights into current job market trends, salary expectations, and career paths from the perspective of hiring managers [19]. - The program provides reliable internal referral opportunities and direct hiring benefits, facilitating quicker access to high-paying job offers [19].