# Embedding fine-tuning

**Source:** https://promtable.com/glossary/embedding-fine-tune

> Embedding fine-tuning adapts a pretrained embedding model to your domain by training on (anchor, positive, negative) triplets — improving retrieval recall on domain-specific terminology that off-the-shelf models miss.

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Embedding fine-tuning adapts a pretrained embedding model to your domain by training on (anchor, positive, negative) triplets — improving retrieval recall on domain-specific terminology that off-the-shelf models miss.

Off-the-shelf embedding models cover general semantic similarity but underperform on domain-specific vocabulary, jargon, and product names. Fine-tuning on a few thousand (anchor, positive, negative) triplets from your real queries and documents materially improves recall — Voyage AI and Cohere both ship managed embedding fine-tuning workflows in 2026. Open-weight fine-tuning is straightforward with SentenceTransformers, training on contrastive losses. The cost-benefit trade-off: fine-tuning embedding models is cheaper than fine-tuning LLMs and the recall improvements compound across every retrieval call.

## When to use

- Production RAG over domain-specific corpora.
- Multilingual corpora where off-the-shelf models miss your language pair.
- Long-tail product / SKU recall.

## Common mistakes

- Training on too few triplets — you need at least ~3,000 for a meaningful lift.
- Forgetting to re-embed the corpus with the fine-tuned model.

## Related terms

- [embeddings](https://promtable.com/glossary/embeddings)
- [fine-tuning](https://promtable.com/glossary/fine-tuning)
- [rag](https://promtable.com/glossary/rag)
- [semantic-search](https://promtable.com/glossary/semantic-search)

*Last updated: 2026-06-01*
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Original page: https://promtable.com/glossary/embedding-fine-tune
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