{"head":{"title":"RAG or Fine-Tuning? Which One Your AI Feature Needs","description":"Use RAG when the model needs your facts and documents; use fine-tuning to change style, format or a narrow skill. How to choose, and when to combine them.","canonical":"https://oceanlab.in/questions/rag-or-fine-tuning"},"crumbs":[{"name":"Home","path":"/"},{"name":"Questions","path":"/questions"},{"name":"Should I use RAG or fine-tuning?","path":"/questions/rag-or-fine-tuning"}],"question":"Should I use RAG or fine-tuning?","answer":["Use ",{"href":"/glossary/rag","text":"RAG"}," when the model needs to know your facts, documents or data, especially if they change. Use ",{"href":"/glossary/fine-tuning","text":"fine-tuning"}," when you need to change how the model behaves, such as a consistent format, tone or a narrow task, and prompting is not enough. Most teams should start with prompting and RAG."],"sections":[{"heading":"Choose RAG when","body":[],"bullets":[["Answers must come from your documents, products, policies or records."],["The information changes and must stay current."],["You need to show sources for each answer."],["Different users may see different data, based on permissions."]]},{"heading":"Choose fine-tuning when","body":[],"bullets":[["You need a consistent output format or style that prompts do not reliably give."],["A narrow, repeated task where a smaller tuned model can match a larger one at lower cost."],["You have enough good examples of the behaviour you want."]]},{"heading":"Start simple","body":[["Try a clear prompt with examples and ",{"href":"/glossary/structured-output","text":"structured output"}," first, then add retrieval. Fine-tune only once ",{"href":"/glossary/llm-evals","text":"evals"}," show a gap that prompting and RAG cannot close. The two can also be combined: a tuned model that follows your format, fed with retrieved facts."]],"bullets":[]}],"faqs":[{"question":"Can fine-tuning teach the model our documents?","answer":"Not reliably. It changes behaviour more than it stores facts, and facts change. Use retrieval for knowledge."},{"question":"Is RAG expensive?","answer":"It adds embedding, storage in a vector database and longer prompts. Usually much cheaper than retraining, and caching helps."},{"question":"Does RAG stop hallucinations?","answer":"It reduces them by giving the model the right facts, but does not remove them. Check answers against sources and test with evals."}],"terms":[{"href":"/glossary/rag","label":"RAG"},{"href":"/glossary/fine-tuning","label":"fine-tuning"},{"href":"/glossary/embedding","label":"embedding"},{"href":"/glossary/vector-database","label":"vector database"},{"href":"/glossary/hallucination","label":"hallucination"},{"href":"/glossary/llm-evals","label":"LLM evals"}],"tools":[{"href":"/tools/fine-tuning-vs-rag","label":"Fine tuning vs RAG","detail":"Free tool"},{"href":"/tools/rag-cost-calculator","label":"RAG cost calculator","detail":"Free tool"},{"href":"/tools/embedding-cost-calculator","label":"Embedding cost calculator","detail":"Free tool"}],"links":[{"href":"/ai-automation","label":"AI automation","detail":"How OceanLab automates company workflows with AI."},{"href":"/ai","label":"AI by industry","detail":"AI use cases, costs and risks per industry."},{"href":"/pricing/categories/llm-api","label":"LLM API pricing","detail":"Model prices side by side, with sources."}],"related":[{"href":"/questions/how-much-does-an-ai-agent-cost","label":"How much does an AI agent cost?","detail":"Question"},{"href":"/questions/how-to-estimate-llm-costs","label":"How do I estimate LLM costs?","detail":"Question"},{"href":"/questions/can-i-send-phi-to-an-llm","label":"Can I send PHI to an LLM?","detail":"Question"},{"href":"/compare/ai-agent-vs-workflow-automation","label":"AI agent vs workflow automation","detail":"Comparison"},{"href":"/compare/rag-vs-fine-tuning","label":"RAG vs fine-tuning","detail":"Comparison"},{"href":"/glossary","label":"Glossary","detail":"Every term, in plain words."}],"sources":[],"legal":false,"updatedAt":"2026-10-25"}