Key takeaways
- Use retrieval to supply changing or private factual knowledge.
- Fine-tuning is usually better suited to behavior or task adaptation.
- Neither replaces grounding tests and production evaluation.
Editorial note: Educational sample content for review; not a newly reported or independently fact-checked news story.
Retrieval-augmented generation
RAG retrieves relevant documents and includes their contents in the model's working context when generating an answer. It can provide access to changing information without modifying model weights.
A robust retrieval pipeline needs chunking, indexing, permissions, ranking and source attribution. Retrieval can still return irrelevant or malicious instructions.
Fine-tuning
Fine-tuning updates model behavior using a task-specific dataset. It may improve consistency with a target response format or domain workflow, but it does not guarantee faithful memorization of facts.
Training and serving costs, dataset rights and regressions must be considered.
A decision checklist
First assess whether better prompts, rules or tool calls are sufficient. If answers need up-to-date source documents, consider retrieval. If the task needs consistent specialist behavior with strong labeled examples, evaluate fine-tuning.
Use an independent benchmark to compare options. It is common to combine methods, but complexity should be justified by measured benefits.
Sources & further reading
Primary sources and learning resources. Verify specific product and research claims directly.
