When is fine-tuning worth considering?
Why C is the answer
When prompts and RAG cannot deliver the style or format you need
Fine-tuning suits behavior, not fresh knowledge. Try simpler options first.
In detail
- Fine-tuning suits style or format that prompts and RAG cannot deliver.
- Try cheaper options first.
Use case
An illustrative scenario, not a real incident.
Prompts and RAG cannot reach the strict report style a firm needs, so fine-tuning becomes worthwhile.
See it in different ways
flowchart LR A["Better prompt"] --> B["Add RAG for knowledge"] B --> C["Fine-tune for style or format"]
stateDiagram-v2 [*] --> CheckRetrieval CheckRetrieval --> TuneChunks: wrong chunks CheckRetrieval --> TunePrompt: right chunks, poor answers TunePrompt --> FineTune: style still not met TuneChunks --> [*] FineTune --> [*]
flowchart TD
Q{"What is missing?"}
Q -->|"Current facts"| A0["RAG"]
Q -->|"Clear instructions"| A1["Better prompts"]
Q -->|"Style that nothing else reaches"| A2["Fine-tuning"]Key point: Fine-tune when prompts and RAG cannot deliver.
Why the other options are wrong
- A. As the first step for every new generative AI project
- It should not be the first step.
- B. When you want to reduce the size of your test dataset
- It has nothing to do with test set size.
- D. When you want the model to know documents that change daily
- Daily changes suit retrieval.