When to Use RAG—and When Not To
RAG is appropriate when an answer needs evidence selected from a corpus at runtime: internal policies, product documentation, support knowledge, or long technical manuals. It helps when data is too large for every prompt or changes more frequently than model training.
Do not use RAG for every task. Exact account state should come from an authorized database query; arithmetic belongs in deterministic code; stable formatting may need only instructions. Fine-tuning changes model behavior but is usually not the right way to keep a changing document collection current. Hybrid applications often combine SQL, APIs, RAG, and model generation.
Implementation example
Exact account balance -> authorized database query
Policy explanation -> RAG + citations
Arithmetic -> deterministic code
Complex research -> bounded multi-step retrieval💡 Select the simplest reliable architecture for the evidence and precision requirements.
Key Takeaways
- Inspect the relevant pipeline stage instead of guessing.
- Preserve source provenance, versions, and access scope.
- Measure quality on representative queries.
Knowledge Check
Which task is usually better served by a direct database query?