How to Build a RAG System

RAG is the standard way to build reliable LLM apps—and the most commonly botched. Here's how to build a RAG system that gives accurate, grounded answers.

What RAG does

Retrieval-augmented generation retrieves relevant context from your data and gives it to an LLM, so answers are grounded in your content—reducing hallucination and enabling source-backed answers.

The pipeline

Step	What matters
1. Ingest & chunk	Split documents thoughtfully
2. Embed	Represent meaning well
3. Retrieve	Surface the right context
4. Rerank	Improve context ordering
5. Evaluate	Measure retrieval and answers

Retrieval quality decides everything

Most RAG failures are retrieval failures: fetch the wrong context and even a great model answers wrongly. Chunking, embeddings, retrieval, and reranking are where quality is won—not model choice. Wiring an LLM to a vector database is the easy 20%.

Evaluate, don't guess

Measure retrieval quality (did it fetch the right context?) and answer quality (was the answer correct?). Without evaluation, you're shipping blind—the discipline that separates production RAG from demos.

Do you need fine-tuning?

Usually no—good retrieval plus a strong general model beats fine-tuning for most cases. See RAG vs fine-tuning.

Why FISTA

FISTA Solutions builds RAG systems where retrieval is measured and tuned—accurate, grounded answers, not confident wrong ones—through AI agents and enablement, backed by 150+ projects across 12+ countries.

Building RAG that gives trustworthy answers? Talk to FISTA.