Implementing a RAG system: Crawl

<p>RAG is not dead. It just got promoted. For years, retrieval-augmented generation helped apps pull the right documents before an AI answered. Now, AI agents need something

<!-- SC_OFF --><div class="md"><p>We spent the last year building a code indexing system and tried pretty much every retrieval approach along the way. Sharing what actually worked

<h1> RAG Systems in Production: Building Enterprise Knowledge Search </h1> <h2> Introduction </h2> <p>Retrieval-Augmented Generation (RAG) has revolutionized how enterprises build

<p>Retrieval-Augmented Generation (RAG) is often described as a simple pipeline:</p> <blockquote> <p>Query → Retrieve documents → Send context to an LLM → Generate answer</p>

<p><a class="article-body-image-wrapper"

<h2> From Overwhelm to Insight: Navigating Codebases with AI </h2> <p>We've all been there. You join a new project, inherit a legacy system, or simply return to your own code

<p>In the rapidly evolving landscape of Generative AI, the transition from experimental Proof of Concepts (POCs) to production-grade applications is the most significant hurdle

<!-- SC_OFF --><div class="md"><p>Hi Reddit, we are a team of database researchers (including a PhD from MIT DB Group) and we just open-sourced an embedded vector database for