Building RAG Systems
Learn to build RAG systems: retrieval-augmented generation, vector databases, embeddings, and knowledge integration.
What is retrieval-augmented generation?
Retrieval-augmented generation (RAG) combines information retrieval with language generation. Before an LLM answers, the application retrieves relevant documents and places them in the model's context so the answer can use current, private, or domain-specific evidence.
In plain language: RAG gives an LLM an open-book reference for each question. It improves grounding without retraining the model and can attach sources to claims.
Types of RAG systems
- Workflow: User Query -> Vector Search -> Retrieve Documents -> Generate Answer
Basic RAG is simple, fast, and architecturally clear. Its limits are weak relevance filtering, bounded context, and little multi-step reasoning. It fits documentation search, simple Q&A, and basic chatbots.
Common RAG challenges
- Problem: retrieved documents do not answer the question.
Common causes are weak embeddings, poor chunking, and inadequate query understanding. Improve it with stronger embeddings, hybrid vector-plus-keyword search, query expansion, and reranking.
RAG system demo
The demo simulates retrieval, evidence-backed generation, source attribution, and confidence display. It does not call an external model or database.
Implementation progression
Embed the question, retrieve the top matching passages, join them into a context, and instruct the model to answer only from that context.
RAG evaluation
Production best practices
- Better retrieval usually beats retrieving more documents.
- Start with basic RAG and add conversational or agentic behavior only when needed.
- Measure retrieval quality separately from answer quality.
- Design explicitly for empty retrieval, conflicting sources, provider failure, and unsupported claims.