What are the components of a RAG system?

Ingestion (loading source documents), chunking (splitting them sensibly), embeddings (turning chunks into vectors), retrieval (finding relevant chunks for a query), generation (the LLM answering with that context), and evaluation (measuring retrieval and answer quality).

What's the most common reason RAG systems underperform?

Poor retrieval and no evaluation. Teams focus on the LLM, but answer quality is mostly determined by whether the right context was retrieved. Without an evaluation harness, you can't tell whether changes help or hurt.

Do I need a vector database for RAG?

Usually yes for scale, though small systems can use simpler stores. Options include Pinecone, Weaviate, pgvector, and Qdrant. The choice depends on scale, latency, existing infrastructure, and whether you need hybrid keyword + semantic search.

The RAG Implementation Guide

Summary

A production RAG system has six parts: ingestion, chunking, embeddings, retrieval, generation, and — most importantly — evaluation. Retrieval quality and evaluation, not the LLM, are what separate a reliable RAG system from a flaky demo.

Retrieval-augmented generation is the most common pattern for getting reliable value from generative AI in the enterprise. This guide walks through building one that actually works in production.

1. Ingestion

Pull content from its sources — documents, wikis, databases, tickets — and normalize it. Plan for keeping it current: a RAG system that answers from stale data is worse than no system. Reliable ingestion is a data engineering problem.

2. Chunking

Split documents into retrievable units. Too large and retrieval is imprecise; too small and answers lose context. Chunk by structure (sections, headings) where possible, and tune size to your content.

3. Embeddings

Convert chunks into vectors with an embedding model. The model choice affects retrieval quality and cost — evaluate options on your content rather than trusting a leaderboard.

4. Retrieval

For each query, retrieve the most relevant chunks. Strong systems use hybrid retrieval (semantic + keyword) and re-ranking, and often query expansion. This stage, not the LLM, drives most of your answer quality.

5. Generation

Pass retrieved context to the LLM with a structured prompt that instructs it to answer only from the provided context and to cite sources. This is what reduces hallucination and makes answers traceable.

6. Evaluation (don’t skip this)

Build an evaluation set of real questions with known good answers. Measure retrieval quality (did we fetch the right context?) and answer quality (was the response correct, grounded, and complete?). Without this, every change is a guess.

RAG vs. fine-tuning

RAG grounds a model in changing, private knowledge; fine-tuning teaches style or a narrow skill. For most knowledge-access use cases, start with RAG. See our full RAG vs. fine-tuning comparison.

Get help

We build production RAG systems end to end — see RAG development or book a consultation.