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RAG vs fine-tuning for a product feature

Fine-tuning is a tool, not a personality. If the facts live in files that change weekly, retrieve. If the model must speak in a tight house style and evals fail without weights, then fine-tune.

Comparaisons

Start with retrieval

Chunk the corpus, embed it, cite the chunks, refuse when nothing matches. You can update a PDF without a training run. This is the default in our AI builds.

Fine-tune when evals demand it

Classification into your taxonomy, or a format the base model will not hold. We keep a frozen eval set so we can say whether the fine-tune earned its keep.

Questions

Can we do both?

Yes. Retrieve for facts, fine-tune for form. That costs more and needs a reason in the quote.

À partir de $1,150 USD · Quoted in 48 hours · env. 1 060 €

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