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): ...

Start with the simplest Search - in-memory embeddings with relevance ranking. End with the most complex planet-scale Search - 70+ corpus mix of token, embeddings, and knowledge graphs, all jointly retrieved, custom ranked, joint re-ranked, and then LLM-processed, at 160,000 queries per second in under 200msec. This talk will be a fun “one query at a time” survey of all techniques in RAG in incremental complexity, showing the limits of each technique and what the next layered one opens up in terms of capabil ...

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): ...

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].

AI models are getting tasked to do increasingly complex and industry specific tasks where different retrieval approaches provide distinct advantages in accuracy, explainability, and cost to execute. GraphRAG retrieval models have become a powerful tool to solve domain specific problems where answers require logical reasoning and correlation that can be aided by graph relationships and proximity algorithms. We will demonstrate how an agent architecture combining RAG and GraphRAG retrieval patterns can bridge ...

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]