Fine-tuning the embedding

The least-discussed way to improve retrieval: fine-tune the embedding, not the model. On a corpus full of domain jargon this is the cheapest win there is.

Raw chunk

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Ask an LLMwhat question does this answer

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(question, chunk) pair

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Train the embedding

A general-purpose embedding model has never seen your company's acronyms, part numbers or internal vocabulary. Synthetic question generation closes that gap without a single hand-written label.

Why it works

• The training pairs come from your corpus, not from a public benchmark.
• Improving the retriever improves every query in the pipeline — unlike a prompt change, it compounds.
• The same synthetic set is reusable as ground truth for retrieval evals.