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].

Core Idea - AI models are increasingly used for complex, industry-specific tasks, where different retrieval approaches offer varying advantages in accuracy, explainability, and cost [1] - GraphRAG retrieval models are a powerful tool for solving domain-specific problems requiring logical reasoning and correlation aided by graph relationships and proximity algorithms [1] - An agent architecture combining RAG and GraphRAG retrieval patterns can bridge the gap in data analysis, strategic planning, and retrieval to solve complex domain-specific problems [1] Technology & Architecture - The architecture combines RAG (Retrieval-Augmented Generation) and GraphRAG retrieval patterns [1]

Core Viewpoint - The article emphasizes the importance of integrating existing technical skills with large model applications to enhance career prospects in the AI era, rather than abandoning current expertise [2][3]. Summary by Sections Current Industry Trends - Many professionals in programming fields are feeling anxious about the rise of large models like GPT and DeepSeek, prompting a need to adapt and learn new skills [2]. - Despite layoffs and salary reductions, the trend towards AI application implementation is expected to continue, presenting opportunities for career advancement and salary increases [3]. Course Offerings - A course titled "Large Model Application Development Practical Training" is introduced, designed to help developers master the complete AI application development process through practical projects and live instruction [3][4]. - The course covers essential technologies such as RAG, AI Agent, and Transformer architecture, structured in five modules from basic to advanced levels [7]. Learning Outcomes - Participants will learn to fine-tune mainstream large models for specific scenarios, utilize domain data for model customization, and understand RAG technology for efficient knowledge retrieval and generation [9]. - The course aims to build skills for developing AI Agents capable of multi-task collaboration and complex problem-solving in various industry applications [9]. Success Metrics - The course has served over 20,000 students, receiving positive feedback for its learning methods and outcomes, with many participants securing high-paying job offers [11]. - The program offers opportunities for networking with product teams, building technical barriers, and avoiding job insecurity, particularly for those approaching career milestones [13]. Additional Benefits - Participants will receive access to real-world case studies and insights into high-demand AI applications, enhancing their practical experience and employability [14][16]. - The course includes direct referral opportunities to companies, increasing the chances of obtaining high-paying positions in the AI field [18].

RAG Architectures - Industry highlights the distinction between Naive RAG and Agentic RAG [1] - Industry emphasizes visual explanations of RAG architectures [1]