Best of RAG — September 2025

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    Article
    Avatar of dailydoseofdsDaily Dose of Data Science | Avi Chawla | Substack·34w

    The Open-source RAG Stack

    A comprehensive guide to building production-ready RAG systems using open-source tools. Covers the complete technology stack from frontend frameworks to data ingestion, including LLM orchestration tools like LangChain and CrewAI, vector databases like Milvus and Chroma, embedding models, and retrieval systems. Also showcases 9 practical MCP (Model Context Protocol) projects for AI engineers, ranging from local MCP clients to voice agents and financial analysts.

  2. 2
    Article
    Avatar of dailydoseofdsDaily Dose of Data Science | Avi Chawla | Substack·36w

    8 Key LLM Development Skills for AI Engineers

    Outlines eight essential skills for AI engineers working with Large Language Models in production environments: prompt engineering, context engineering, fine-tuning, RAG systems, agents, deployment, optimization, and observability. Each skill covers practical techniques from crafting structured prompts to implementing monitoring systems, with emphasis on moving beyond basic prompting to building scalable, production-grade LLM applications.

  3. 3
    Article
    Avatar of heidloffNiklas Heidloff·34w

    Leveraging Docling in Langflow

    Docling and Langflow are open-source frameworks for document extraction and building AI agents. The integration allows converting PDFs, DOCX, and slides into structured data through Langflow's visual interface. Documents are processed through Docling components, converted to various formats, chunked for semantic search, and used in RAG workflows with vector databases like Astra for question-answering applications.

  4. 4
    Article
    Avatar of ergq3auoeReinier·35w

    Context Engineering, Clearly Explained

    Context engineering is a framework that encompasses prompts, memory, files, tools, and retrieval-augmented generation (RAG) to optimize how large language models generate responses. Unlike prompt engineering which focuses solely on input text, context engineering considers the entire information ecosystem that influences AI outputs, providing a more comprehensive approach to building reliable agentic systems and improving AI conversation consistency.

  5. 5
    Article
    Avatar of dailydoseofdsDaily Dose of Data Science | Avi Chawla | Substack·33w

    Get Free Lifetime Access to Our Premium Resources

    A comprehensive 10-step roadmap for becoming a full-stack AI engineer, covering everything from coding fundamentals and Python basics to advanced topics like LLM APIs, RAG systems, AI agents, production deployment, observability, security, and advanced workflows. The roadmap progresses from beginner concepts to expert-level implementation of production-ready AI systems.

  6. 6
    Article
    Avatar of dailydoseofdsDaily Dose of Data Science | Avi Chawla | Substack·32w

    ​6 Popular Agentic Design Patterns Used in AI Products!​

    Explores six key agentic design patterns that power modern AI systems: ReAct (reasoning and action), CodeAct (direct code execution), Modern tool use (external API integration), Self-reflection (error correction), Multi-agent workflows (specialized agent collaboration), and Agentic RAG (intelligent data retrieval). Each pattern addresses specific challenges in AI agent development, with real-world examples from companies like CrewAI, Cursor, and Perplexity demonstrating their practical applications.

  7. 7
    Article
    Avatar of dailydoseofdsDaily Dose of Data Science | Avi Chawla | Substack·34w

    Building a Context Engineering Workflow

    Context engineering involves creating dynamic systems that provide LLMs with the right information, tools, and format to complete tasks effectively. This tutorial demonstrates building a multi-agent research assistant that gathers context from four sources: documents, memory, web search, and ArXiv. The workflow uses Tensorlake for document processing, Milvus for vector storage, Zep for memory management, and Firecrawl for web scraping, orchestrated through CrewAI agents that filter and synthesize responses.

  8. 8
    Article
    Avatar of dailydoseofdsDaily Dose of Data Science | Avi Chawla | Substack·35w

    [Hands-on] MCP-powered Agentic RAG

    A step-by-step implementation guide for building an Agentic RAG system using the Model Context Protocol (MCP). The system combines vector database search with web search fallback, using Firecrawl for web scraping, Qdrant as the vector database, and Cursor as the MCP client. The tutorial covers setting up an MCP server, creating tools for both vector DB queries and web search, and integrating everything with Cursor for intelligent query routing.

  9. 9
    Article
    Avatar of meilisearchMeilisearch·35w

    Adaptive RAG explained: What to know in 2025

    Adaptive RAG improves upon traditional Retrieval-Augmented Generation by dynamically deciding when and how to retrieve information based on query complexity. Unlike standard RAG that blindly retrieves documents for every query, adaptive RAG analyzes questions first, skips retrieval for simple prompts, and uses multiple search strategies for complex queries. The approach includes query classification, strategy selection, and iterative retrieval, offering benefits like improved accuracy, better efficiency, and reduced hallucinations. Common applications include customer support systems, conversational AI, and knowledge management platforms.

  10. 10
    Video
    Avatar of TechWithTimTech With Tim·33w

    How to Build a Production-Ready RAG AI Agent in Python (Step-by-Step)

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    Article
    Avatar of meilisearchMeilisearch·34w

    What is agentic RAG? How it works, benefits, challenges & more

    Agentic RAG enhances traditional retrieval-augmented generation by adding agent-like decision-making capabilities. Instead of static retrieval, it uses planning, validation, and iterative evaluation to determine when and how to retrieve context. The system works through query understanding, retrieval planning, context evaluation, and feedback loops. Key benefits include improved accuracy, better reasoning, and explainability, while drawbacks involve higher computational costs and implementation complexity. Common tools include LangChain, LlamaIndex, and Meilisearch for building these intelligent pipelines.

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    Article
    Avatar of phProduct Hunt·35w

    Vectorize: Build RAG pipelines that are optimized for your data.

    Vectorize 2.0 is a data platform that optimizes RAG pipelines by evaluating the best vectorization methods for your data and providing cloud-scale pipeline management. The new version introduces hosted chat agents, no-code chatbots, one-line website integration, real-time data syncing, and hybrid search with knowledge graphs. The platform handles vector database population and keeps data fresh automatically.

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