AI Answer Library
Short answer
For most enterprise knowledge bases, start with RAG and do not fine-tune first. RAG controls what the model can see — answers stay traceable to a source, updated documents take effect the same day, and access control can follow the user. Fine-tuning controls how the model talks: tone, terminology and output format. It does not reliably teach new facts. The two compose well: run RAG first, then add fine-tuning once your evaluation set shows a persistent style or format problem.
Split the complaint into two sentences. "The model does not know this" is a knowledge problem and belongs to RAG. "The model knows, but it does not sound like us" is an expression problem and belongs to fine-tuning. Most complaints phrased as "the model does not understand our business" turn out to be retrieval problems — bad chunking, no metadata filtering, irrelevant passages recalled. Fine-tuning cannot fix those, because it cannot invent a contract clause the model never saw.
| Dimension | RAG (retrieval-augmented) | Fine-tuning | Practical guidance |
|---|---|---|---|
| Injecting new knowledge | Strong — anything retrieved can be used | Unreliable — facts blur or get forgotten | Default to RAG for knowledge needs |
| Update latency | Edit the document, effective immediately | Requires retraining and redeployment | Policies, prices and inventory must go through RAG |
| Source attribution | Can return the source passage | No citation, only an answer | Mandatory for compliance, legal and clinical use |
| Access control | Filter by user or department at retrieval time | Knowledge baked into weights cannot be hidden per user | With tiered permissions, RAG is the only option |
| Style and output format | Constrained by prompt; drifts as prompts grow | Strong — reproduces fixed structures reliably | Consider fine-tuning only after repeated format failures |
| Cost of one change | Low — edit data or chunking strategy | High — new dataset plus a training run | While requirements are still moving, do not fine-tune |
Three situations justify fine-tuning. First, the output structure must be perfectly stable — for example a fixed-field JSON consumed by a downstream system, where prompt constraints occasionally drift. Second, the domain has unusual phrasing that a general model renders awkwardly: industrial process documents, clinical notes, legal drafting. Third, your system prompt has swollen to thousands of tokens that you pay for on every call; distilling those rules into the weights cuts per-call cost meaningfully. All three share a prerequisite: you already have a curated set of human-verified samples, not a raw dump of historical tickets.
Step one: collect 50–100 questions your colleagues actually asked, with reference answers. That evaluation set is the basis of every later judgement. Step two: build RAG only, and tune chunking, metadata and ranking until performance on the evaluation set converges instead of swinging with every single change. Step three: classify the failures by hand — if the right passage was never retrieved, it is a retrieval problem, keep improving RAG; only cases where retrieval was correct but the answer was malformed are fine-tuning candidates. Step four: if you do fine-tune, validate with a lightweight LoRA-style run on a self-hostable open family (Llama 4 / Qwen 3.x / DeepSeek V4 / GLM-5.x) before considering full-parameter training.
Where this applies
People also ask
How accurate can an enterprise RAG knowledge base actually be?
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