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RAG or Fine-Tuning: Which Approach Fits?
Retrieval and fine-tuning solve different problems. Choosing between them starts with understanding what needs to change.
By RaydyantRaydyant Insights
Retrieval augmented generation helps a model use current, private, or changing information at response time. Fine-tuning changes how a model behaves through additional training examples.
Many products benefit from starting with retrieval and evaluation. It keeps knowledge easier to update while giving the team evidence about where more specialized training might be valuable.
Good digital work keeps learning after launch. The ideas in this article are starting points for a conversation about the people, workflows, and outcomes behind your product.
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