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Building RAG Systems: From Architecture to Hybrid Retrieval and Agentic AI

By PulseAugur Editorial · [6 sources] ·

This cluster details the architecture and implementation of Retrieval-Augmented Generation (RAG) systems, focusing on how to build them from first principles. The articles explain RAG as a method to enhance Large Language Models (LLMs) by providing them with relevant context retrieved from external knowledge bases. Key components discussed include knowledge pipelines for ingesting and processing data, and query pipelines for retrieving information and augmenting LLM prompts. Various techniques like hybrid retrieval combining vector search (FAISS) and keyword search (BM25), document chunking, and agentic AI integration are explored to improve answer accuracy and grounding. AI

IMPACT Provides a foundational understanding of RAG systems, crucial for developing more capable and context-aware AI applications.

RANK_REASON The cluster discusses the technical architecture and implementation details of RAG systems, including specific algorithms and components, which falls under research and development in AI.

Read on Towards AI →

AI-generated summary · Google Gemini · from 6 sources. How we write summaries →

Building RAG Systems: From Architecture to Hybrid Retrieval and Agentic AI

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The cluster discusses the technical architecture and implementation details of RAG systems, including specific algorithms and components, which falls under research and development in AI.
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6 independent sources
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product, infra
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29 days old
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COVERAGE [6]

  1. Towards AI TIER_1 English(EN) · Kabir Raj Singh ·

    Production-Ready RAG Architecture: Core Patterns Explained

    <h4><em>Companion post to the video. This is the deeper reference version — the configs, code, and sources the video didn’t have time for. If you just want the mental model, watch the video first; come back here when you’re actually building.</em></h4><h3>Why RAG demos fall apart…

  2. Medium — MCP tag TIER_1 English(EN) · tanvik reddy ·

    Building an MCP-Powered Agentic RAG System: From Retrieval to Self-Correcting Answers

    <div class="medium-feed-item"><p class="medium-feed-image"><a href="https://medium.com/@tanvikreddy24/building-an-mcp-powered-agentic-rag-system-from-retrieval-to-self-correcting-answers-23f483b6d01c?source=rss------mcp-5"><img src="https://cdn-images-1.medium.com/max/1536/1*8img…

  3. dev.to — LLM tag TIER_1 English(EN) · SONU OLIKKARA SABU ·

    I Built an Agentic Hybrid RAG System with FAISS and BM25

    <p>I've been learning about <strong>Retrieval-Augmented Generation (RAG)</strong> and wanted to understand how it actually works by building something myself.</p> <p>Instead of just following tutorials, I decided to create a small project where I could experiment with document re…

  4. dev.to — LLM tag TIER_1 English(EN) · Timilehin Olusegun ·

    Building My First RAG System: From Components to Knowledge and Query Pipelines - Part Two

    <p>In the last <a href="https://dev.to/timilehin-olusegun/building-my-first-rag-system-deriving-the-architecture-from-first-principles-part-one-n43">part</a>, we worked up from the foundational problem to the key components of a RAG system. We examined eight components and how th…

  5. dev.to — LLM tag TIER_1 English(EN) · Timilehin Olusegun ·

    Building My First RAG System: Deriving the Architecture from First Principles - Part One

    <h2> Intro </h2> <p>I recently read an <a href="https://africa.businessinsider.com/news/im-a-vc-who-finds-dollar120-worth-of-ai-subscriptions-more-productive-than-an-intern/t0963k1" rel="noopener noreferrer">article</a> about a VC who uses AI to boost his productivity. He describ…

  6. dev.to — LLM tag TIER_1 English(EN) · Melvin Sabu ·

    Building a Hybrid RAG System with FAISS, BM25, and Agentic AI

    <p>As part of my AI Engineering journey, I recently worked on a project that helped me understand how Retrieval-Augmented Generation (RAG) works in practice.</p> <p>I built a Hybrid RAG system that combines FAISS vector search and BM25 keyword search to retrieve relevant informat…

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