Fine-tuning is the process of taking a model that has already been trained on a large general dataset and continuing its training on a smaller, domain-specific dataset, so it performs better on your particular task. Rather than training from scratch, you adapt an existing model’s broad knowledge to a narrower purpose.
In the context of vector search, fine-tuning most often refers to adapting an embedding model. A general-purpose embedding model may not capture the nuances of a specialised domain — legal contracts, medical records, or a company’s internal jargon — where terms carry meanings that differ from everyday usage. Fine-tuning on domain examples teaches the model to place those domain concepts correctly in vector space, improving retrieval relevance.
The benefit is higher-quality, domain-aware embeddings; the cost is the effort of assembling training data and running the training, plus the need to re-embed your entire corpus afterward, since the new model produces a different vector space incompatible with the old vectors. Fine-tuning pays off most when a general model’s retrieval quality is clearly limiting and you have good domain data to learn from.