[模型微调] What business scenarios are suitable for fine-tuning large models, and when is it appropriate to choose large model fine-tuning?

There are three ways to handle how large models answer specific content: prompt engineering, RAG, and fine-tuning. In many cases, when we face situations where prompt engineering cannot achieve the required answer logic, we think of fine-tuning the large model. In fact, this is a misconception: fine-tuning a large model cannot guarantee that it will answer according to your training data, and in better cases it will be half correct and half wrong.

If you are in industries such as law, government affairs, healthcare, or education that require verbatim rigor, then fine-tuning is not suitable. Your choice should be RAG, using a knowledge base and AI semantic matching algorithms, rather than fine-tuning a large model.

Large model fine-tuning is suitable for learning our specific knowledge, word style, and emotional annotations. We have summarized the usage scenarios suitable for fine-tuning large models.

1. Clients with low requirements for answer accuracy, such as emotional, consulting, and companion chat clients, can rely solely on fine-tuning.
2. When you are dissatisfied with the language style of the answers, for example, when a general large model's answers lack personality and you need a specific persona.
3. When your field is a niche field with many proper nouns, fine-tuning can make the answers more professional.
4. When you need to summarize and polish the Q&A repository, rather than returning the correct answer unchanged.

The business scenarios we face in real life are complex and diverse. Usually, a single choice cannot meet our actual needs; more often, the choice should be to use these three methods in combination.