When to Use RAG (and When Not To)

RAG is the default for grounding AI in your data—but not always the answer. Here's when to use it, when fine-tuning or long context wins, and how to choose.

When to use RAG

Use RAG when you need AI to answer from your own, large, or frequently changing knowledge:

Need	RAG fits
Answer from your documents	Yes
Large knowledge base	Yes
Frequently changing data	Yes
Citable, verifiable answers	Yes

It makes answers grounded, current, and citable—the fix for hallucination.

When not to use RAG

Situation	Instead
Knowledge fits in the prompt	Just include it
Need behavior/style change	Fine-tuning
No external knowledge needed	Plain prompt

Applying RAG reflexively when the knowledge fits in the context window adds needless complexity.

RAG vs fine-tuning

For adding knowledge, RAG usually wins—grounding in current data without training. Fine-tuning is better for behavior, style, or format. They solve different problems—see fine-tuning vs RAG and prompt engineering vs fine-tuning—and are sometimes combined.

The default, applied thoughtfully

RAG is the right default for most knowledge-grounding needs—but match it to the actual need, the right-tool discipline. When you do use it, retrieval quality decides success.

Why FISTA

FISTA Solutions applies RAG where it fits—and simpler or different approaches where they win—so you ground AI accurately without over-engineering, through AI enablement, backed by a verified 99.9% uptime record.

Deciding whether you need RAG? Talk to FISTA.