Overview - The content is a recommendation to reshare insightful information about DS (Data Science), ML (Machine Learning), LLMs (Large Language Models), and RAGs (Retrieval-Augmented Generation) [1] Resource Sharing - Avi Chawla shares tutorials and insights daily on DS, ML, LLMs, and RAGs [1] Topic Focus - The content highlights a clear explanation (with visuals) of Naive RAG vs Agentic RAG [1]

RAG Systems - Agentic RAG systems enhance robustness by aligning individual outcomes with the overall goal [1] - The provided diagram represents one of many possible blueprints for an agentic RAG system [2] - The specific implementation of an agentic RAG system can be adapted to fit particular use cases [2]

RT Avi Chawla (@_avichawla)6 no-code LLMs, Agents, and RAG builder tools for AI engineers:(open-source and production-grade) ...

6 no-code LLMs, Agents, and RAG builder tools for AI engineers:(open-source and production-grade) ...

Context Engineering Overview - Context engineering is defined as the art and science of filling the context window with the right information at each step of an agent's trajectory [2][4] - The industry categorizes context engineering strategies into writing context, selecting context, compressing context, and isolating context [2][12] - Context engineering is critical for building agents because they typically handle longer contexts [10] Context Writing and Selection - Writing context involves saving information outside the context window, such as using scratch pads for note-taking or memory for retaining information across sessions [13][16][17] - Selecting context means pulling relevant context into the context window, including instructions, facts, and tools [12][19][20] - Retrieval-augmented generation (RAG) is used to augment the knowledge base of LLMs, with code agents being a large-scale application [27] Context Compression and Isolation - Compressing context involves retaining only the most relevant tokens, often through summarization or trimming [12][30] - Isolating context involves splitting up context to help an agent perform a task, with multi-agent systems being a primary example [12][35] - Sandboxing can isolate token-heavy objects from the LLM context window [39] Langraph Support for Context Engineering - Langraph, a low-level orchestration framework, supports context engineering through features like state objects for scratchpads and built-in long-term memory [44][45][48] - Langraph facilitates context selection from state or long-term memory and offers utilities for summarizing and trimming message history [50][53] - Langraph supports context isolation through multi-agent implementations and integration with sandboxes [55][56]

AI Agents and Memory - The presentation focuses on the importance of memory in AI agents, emphasizing that memory is crucial for making agents reflective, interactive, proactive, reactive, and autonomous [6] - The discussion highlights different forms of memory, including short-term, long-term, conversational entity memory, knowledge data store, cache, and working memory [8] - The industry is moving towards AI agents and agentic systems, with a focus on building believable, capable, and reliable agents [1, 21] MongoDB's Role in AI Memory - MongoDB is positioned as a memory provider for agentic systems, offering features needed to turn data into memory and enhance agent capabilities [20, 21, 31] - MongoDB's flexible document data model and retrieval capabilities (graph, vector, text, geospatial query) are highlighted as key advantages for AI memory management [25] - MongoDB acquired Voyage AI to improve AI systems by reducing hallucination through better embedding models and re-rankers [32, 33] - Voyage AI's embedding models and re-rankers will be integrated into MongoDB Atlas to simplify data chunking and retrieval strategies [34] Memory Management and Implementation - Memory management involves generation, storage, retrieval, integration, updating, and forgetting mechanisms [16, 17] - Retrieval Augmented Generation (RAG) is discussed, with MongoDB providing retrieval mechanisms beyond just vector search [18] - The presentation introduces "Memoriz," an open-source library with design patterns for various memory types in AI agents [21, 22, 30] - Different memory types are explored, including persona memory, toolbox memory, conversation memory, workflow memory, episodic memory, long-term memory, and entity memory [23, 25, 26, 27, 29, 30]

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):Check this!! You can now raise PRs by just writing English prompts.I integrated the @codegen coding agent with Slack and asked it to build a video RAG app.5 minutes later, it raised a PR with fully working code.Completely hands-off! https://t.co/bcQFp6yBDJ ...

Core Viewpoint - The article emphasizes that 2025 marks a significant turning point for AI Agents, transitioning from "toys" to "tools" as various successful Agent projects emerge and major companies release MCP protocol support [1]. Group 1: Development and Features of AI Agents - Many Agent projects are still stuck in the POC stage, facing challenges such as long development cycles and difficulty in validating commercial value [2]. - Alibaba Cloud's new upgrade of Bailian 3.0 provides a comprehensive solution for developers, addressing all needs for large model applications and Agent development [2][12]. - The introduction of the "Agent tipping" feature allows users to reward Agents they find useful, enabling direct monetization for developers [3][4][5]. Group 2: Agent Store and Templates - The Agent Store has officially launched, offering hundreds of Agent templates across various industries, allowing developers to quickly start secondary development projects [7][10][18]. - Developers can easily copy Agent configurations and validate their usability, streamlining the development process [21]. Group 3: Enhanced Capabilities and Tools - The upgrade includes a full suite of capabilities from model supply to application data and development tools, enhancing the overall development experience [13][15]. - The new multi-modal RAG capability supports processing complex enterprise documents, significantly improving document handling capabilities [29][30]. - The introduction of V-RAG allows for better content recognition in structured documents, enhancing the effectiveness of document processing [33][34]. Group 4: MCP Service Enhancements - The MCP service has been upgraded to support KMS encryption, addressing key management issues and reducing risks associated with plaintext exposure [36][37]. - Over 50 enterprise-level MCPs have been launched, with more than 22,000 users utilizing these services to create over 30,000 MCP Agents [41]. Group 5: Multi-modal Interaction Development Kit - The multi-modal interaction development kit provides low-cost development capabilities for enterprises, enabling a new generation of intelligent user experiences [45]. - This kit supports various devices and applications, allowing for flexible integration of multi-modal capabilities [47][48]. Group 6: Commercialization and Sustainability - The introduction of the Agent tipping feature opens new pathways for developers to monetize their creations, establishing a sustainable ecosystem for AI Agents [50][51]. - Alibaba Cloud's exploration serves as a reference for the industry, showcasing a viable commercialization model for AI applications [52].

Core Viewpoint - Context engineering is emerging as a crucial skill for AI engineers, shifting the focus from traditional prompt engineering to providing structured and dynamic context for large language models (LLMs) to perform tasks effectively [3][7][15]. Group 1: Definition and Importance of Context Engineering - Context engineering involves constructing dynamic systems that provide accurate information and tools in the right format, enabling LLMs to complete tasks effectively [9][10]. - The significance of context engineering lies in its ability to address common failures in AI systems, which often stem from inadequate context or incorrect information being provided to the model [12][15]. - Unlike prompt engineering, which focuses on crafting clever prompts, context engineering emphasizes the importance of delivering complete and structured context to enhance model performance [17][19]. Group 2: Components of Effective Context Engineering - Effective context engineering requires accurate information, as models cannot infer context without being explicitly provided with it [12][19]. - The format of the context is critical; how information is communicated to the LLM can significantly impact its responses [13][19]. - Tools must be appropriately utilized to access external information, and the returned data should be formatted in a way that is easily understandable by the LLM [20]. Group 3: Transition from Prompt Engineering to Context Engineering - The transition from prompt engineering to context engineering is driven by the increasing complexity of applications, highlighting the need for a more comprehensive approach to context provision [16][17]. - Context engineering can be viewed as a subset of prompt engineering, where the focus shifts from single input prompts to managing and formatting dynamic data sets [17][18].

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):Finally! A RAG over code solution that actually works (open-source).Naive chunking used in RAG isn't suited for code.This is because codebases have long-range dependencies, cross-file references, etc., that independent text chunks just can't capture.Graph-Code is a https://t.co/c6ISqmauSX ...