RAG vs Fine-tuning: Which AI approach do you need?
The business decision
When businesses want an AI to answer questions based on their own data, they typically choose between Retrieval-Augmented Generation (RAG) and Fine-tuning. Fine-tuning involves retraining a language model on your dataset to change its underlying behavior, style, or knowledge. RAG, on the other hand, leaves the model alone and instead searches your documents in real-time, providing the most relevant snippets to the model to generate an answer.
A practical first scope
For most business knowledge use cases—like querying HR policies, product manuals, or legal contracts—RAG is significantly more practical and accurate. RAG allows you to instantly update or delete information by simply removing the document from the database. If you use fine-tuning, updating facts requires retraining the model, which is slow, expensive, and error-prone.
How to validate it
Another critical advantage of RAG is access control and citations. A RAG system can verify a user's permissions before retrieving a document, ensuring that employees only see answers based on files they are authorized to access. It can also provide direct citations to the source document. Fine-tuned models cannot reliably cite their sources and are prone to 'hallucinating' facts.
When to choose another approach
Fine-tuning is better suited for teaching a model a new language, a specific tone of voice, or a highly specialized output format (like generating proprietary code). But if your goal is accurate information retrieval over a changing knowledge base, RAG development is the right technical path.
A proposal should make the boundaries clear.
We scope deliverables, assumptions, exclusions, acceptance checks, responsibilities and support separately. No fixed price or launch date is promised before the requirements are reviewed.
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