Tools/RAG vs Fine-tune Decision Tool
RAG vs Fine-tune Decision Tool
Not sure whether to ground your model in your own knowledge (RAG) or train its behaviour (fine-tuning)? Answer five questions and get a clear recommendation on where to start. Free, no signup. Guidance, not gospel.
1. What are you mainly trying to fix?
2. What does your data look like?
3. How often does the underlying knowledge change?
4. Do you need answers traceable to a source?
5. What is your appetite for ongoing ML effort?
Answer all five questions to see your recommendation.
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Frequently Asked Questions
They are complementary, not exclusive. Many production systems use RAG for fresh, traceable knowledge and light fine-tuning for consistent tone or format. This tool tells you where to start, not what to use forever.
When the model needs current, private or frequently changing knowledge, when answers must be traceable to a source, and when you want to avoid maintaining training pipelines. RAG updates by changing the documents, not retraining the model.
When you need consistent behaviour, tone or output format, you have a few thousand good input and output examples, and the underlying knowledge is stable. Fine-tuning shapes how the model responds rather than what facts it can reach.
Often no. Strong prompting on a capable base model is the cheapest way to validate a use case. Add RAG or fine-tuning once you have evidence of where the base model falls short.
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