# Domain-tuned embeddings for better search

> Fine-tune an embedding model on your terminology so retrieval finds the right document first: the biggest lever on RAG quality.

Source: https://aibyos.com/research/embed

Research · Machine learning

# Domain-tuned embeddings for better search

Fine-tune an embedding model on your terminology so retrieval finds the right document first: the biggest lever on RAG quality.

**+10–30%**recall@10 over generic embeddings

**smaller**index and faster search

**own**model, no embedding API costs

## Pipeline

1.  Mine query–document pairs from logs and documents
2.  Generate synthetic questions with an LLM
3.  Contrastive fine-tuning with hard negatives
4.  Evaluate recall/MRR on a labelled set
5.  Re-index and A/B test in production

Figures show the typical order of magnitude for this approach compared with calling a large general-purpose model. Actual results depend on the task and data; we measure them on your data during the baseline phase.

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## Typical tooling

-   Sentence Transformers
-   pgvector / Qdrant
-   Ragas

## Related

[All research](https://aibyos.com/research)[Sovereign GPU compute](https://aibyos.com/gpu)[Fine-tuning](https://aibyos.com/services/fine-tuning)