Teams reach for fine-tuning far earlier than they should. In almost every engagement we run, a well-built retrieval pipeline outperforms a fine-tuned model at a fraction of the cost.
The cost curve
Fine-tuning locks knowledge into weights. Every content change means another training run, another evaluation pass, and another deployment. Retrieval keeps knowledge in a store you can update in seconds.
Auditability
When a retrieval system answers, you can point at the documents it used. When a fine-tuned model answers, you are guessing. For regulated industries that difference decides the architecture.
When fine-tuning does win
Tone, format and structured output are genuinely learned behaviours. If you need a model to always emit valid JSON in your schema, or to write in a specific voice, fine-tuning earns its keep.
