Fine tuning is for form, not facts

Fine-tuning is one approach to domain-specific model refinement (DSMR), but it's not a silver bullet for improving domain-specific performance. When customers ask about fine-tuning, they're often describing a job that involves improving the quality of the model's output based on their specific application needs. However, fine-tuning typically results in creating a niche model for a niche use-case, and it's most effective when the data is plentiful and easy to come by. The approach doesn't work well when it comes to facts and hallucination. Instead, there are broader DSMR techniques available, including prompt refinement, example selection, retrieval assisted generation, and reinforcement learning with human feedback. These approaches can help solve problems that require incorporating new concepts into the internal knowledge graph represented inside the neural network.

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