The RAG cluster
RAG: The Complete Guide
Retrieval-Augmented Generation is the most common pattern in production AI applications, because it directly solves the two biggest weaknesses of language models: outdated knowledge and hallucination on specific facts.
This guide covers RAG from first principles through the real architectural decisions — chunking, embeddings, vector databases, and reranking — that separate a working demo from a system people can actually trust.
In this guide
6 articles, organized by question.
Foundations
What RAG is, how it is built, and when to use it instead of fine-tuning.
What Is RAG?
The single most common pattern in production AI applications, explained from first principles.
7 min readRetrieval & groundingRAG Architecture Explained
Beyond the basic concept: the real components, decisions, and failure points in a working RAG pipeline.
8 min readRetrieval & groundingRAG vs Fine-Tuning
Two different ways to adapt a model to your data, with very different costs and trade-offs.
7 min readThe data and retrieval layer
The infrastructure that makes retrieval accurate and fast.
What Are Embeddings?
The quiet technology underneath RAG, semantic search, and recommendation systems.
6 min readRetrieval & groundingWhat Is a Vector Database?
The specialized storage layer that makes fast, meaning-based search possible at scale.
6 min readRetrieval & groundingSemantic Search Explained
Search that understands intent, not just matching words — and how to build it.
6 min readFrequently asked
Before you dive in.
RAG (Retrieval-Augmented Generation) retrieves relevant information from a knowledge source and feeds it to a language model before it answers, grounding the response in real data instead of memory alone.
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