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
smallerindex and faster search
ownmodel, 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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