The difference in plain language
With RAG, an application searches a knowledge source and includes relevant passages in the model context for that request. The source can be updated without training the model again. Retrieval quality, access controls and answer checks still matter; the model may misunderstand a passage.
Fine-tuning trains a supported base model on curated examples so it is more likely to follow a desired pattern. It can shape behavior, but is not a dependable substitute for a live source of current policies, prices or customer records. Updates require managing new data and a model version, and the result still needs evaluation.
Choose by the job
Use this as a starting point, then test on your own examples:
- Choose RAG when answers depend on a changing handbook, product catalogue or internal documentation and readers need supporting sources.
- Consider fine-tuning when a model repeatedly misses a stable instruction, output format or task pattern despite clear prompts.
- Use ordinary code or a database lookup for precise values such as inventory or account balances. Do not ask a generative model to invent what a system can retrieve exactly.
- Combine methods only when each has a defined role: retrieval supplies current evidence; a tuned model follows a consistent task pattern. More components mean more failure modes to monitor.
A practical way to decide
Create a small evaluation set of representative questions and expected outcomes before building a large system.
Define a good answer
Write down who will use the assistant, what it may answer, what it must escalate and how you will score correctness, source quality, latency and cost.
Build a prompt-only baseline
Try clear instructions and a few representative examples. This simple benchmark often solves formatting or scope problems without extra infrastructure.
Add retrieval for missing knowledge
Retrieve relevant, permission-checked passages and ask the model to answer from that evidence. Test whether the right passages are found, not only whether the prose sounds confident.
Label failures
Separate missing evidence, irrelevant retrieval, misunderstood instructions, inconsistent format and unsupported claims. Fix the source or retrieval when evidence is the issue; refine instructions when behavior is the issue.
Evaluate tuning only for a repeatable behavior gap
Check that you have high-quality examples and a separate holdout set. Compare the candidate with the baseline on the same tasks.
Plan updates and monitoring
Decide who can access sources, how updates are indexed, how versions are rolled back and how user data is handled. Avoid logging sensitive content unnecessarily.
Common mistakes
A wrong answer does not automatically mean a model needs fine-tuning. First check whether the right facts were available and instructions were clear. RAG can fail because a document is missing, filtered incorrectly or ranked below irrelevant passages. Fine-tuning can fail when examples are inconsistent or evaluation covers only familiar cases.
Keep evaluation questions separate from training examples, include edge cases and have a domain expert review outputs. A confident tone is not evidence. For sensitive use cases, define human escalation and a way to report errors.
Start with the smallest testable design
Define the task, build a prompt-only baseline and measure it. Add RAG for grounded, changing information. Consider fine-tuning when measurable behavior errors remain. Decide using evidence, not fashion.