Advanced9 min
Fine-Tuning Embeddings
Fine-tune embedding models on your domain data to improve retrieval quality by 10-25% — using contrastive learning with query-document pairs from your actual search logs.
Quick Reference
- →Off-the-shelf embeddings work well for general text but underperform on domain-specific terminology
- →Fine-tuning: train on (query, relevant_doc, irrelevant_doc) triplets from your domain
- →Data source: search logs with click-through data, annotated query-document relevance pairs
- →Frameworks: sentence-transformers (Python), OpenAI fine-tuning API, Cohere custom models
- →Expected improvement: 10-25% better retrieval recall on domain queries
- →When NOT to fine-tune: <1000 training pairs, general-purpose queries, rapidly changing content
When Fine-Tuning Helps
| Scenario | Fine-Tune? | Why |
|---|---|---|
| Legal document retrieval | ✅ Yes | Legal terminology differs from general language |
| Medical Q&A | ✅ Yes | Medical terms, drug names, conditions have domain-specific relationships |
| Internal company docs | ✅ Maybe | If jargon is heavy and retrieval is poor |
| General knowledge Q&A | ❌ No | Off-the-shelf models handle this well |
| Rapidly changing content (news) | ❌ No | Fine-tuned model can't keep up with new terms |
| Small corpus (<100 docs) | ❌ No | Not enough data to fine-tune meaningfully |