A model must learn your company's writing style. Which approach is the most direct?
Why A is the answer
Fine-tuning
Fine-tuning adapts behavior. RAG adds knowledge but does not change style as directly.
In detail
- Fine-tuning adapts a model to a style.
- Use RAG for knowledge, and fine-tuning for behavior.
Use case
An illustrative scenario, not a real incident.
A publisher wants every draft to sound like its own style guide. Fine-tuning adapts the model to that style.
See it in different ways
flowchart LR A["In-context learning: prompts"] --> B["RAG"] B --> C["Fine-tuning"] C --> D["Pre-training"]
stateDiagram-v2 [*] --> InContext: prompt examples InContext --> Rag: needs your data Rag --> FineTuning: needs your style FineTuning --> PreTraining: rarely, a new model
flowchart TD
Q{"What is missing?"}
Q -->|"Knowledge"| A0["RAG"]
Q -->|"Style or behavior"| A1["Fine-tuning"]Key point: Fine-tuning adapts behavior.
Why the other options are wrong
- B. Changing the AWS Region
- Region has no effect on style.
- C. Adding an IAM policy
- A policy controls access.
- D. Raising the maximum output length
- Length is not style.