---
type: concept
title: "Fine-Tuned Embeddings"
description: "Training embedding models on domain data so semantic search retrieves the right chunks for a specific corpus."
tags: [embeddings, fine-tuning, retrieval]
videos: [every-rag-strategy-explained-in-13-minutes-no-fluff]
created: 2026-07-21
updated: 2026-07-21
---

# Fine-Tuned Embeddings

Fine-tuned embeddings are embedding models retrained on domain-specific data so that similarity in vector space reflects what "similar" means for your particular corpus. As Cole explains, "what you can do with embedding models, just like large language models, is fine-tune them on a domain specific data set like for legal or for medical" [0:11:04]. A general-purpose embedding model captures broad semantic similarity, but a specialized domain often needs its own notion of relatedness that the base model never learned.

## Why it matters

Off-the-shelf embeddings encode general language similarity, which can miss or even invert the associations a domain cares about. Cole gives a customer-support example: after fine-tuning, "my order was late is going to be most like items are always sold out because now it's based more on sentiment" [0:11:32]. To a generic model those two phrases share little surface vocabulary, but for a support-ticket corpus they express the same underlying frustration, so they should retrieve together. Fine-tuning shifts the geometry of the vector space toward the relationships that matter for retrieval in that domain, improving which chunks come back for a given query.

## Builds on

- [Vector Embeddings](./vector-embeddings.md) - fine-tuning adapts the embedding model that produces these vectors; the mechanism is unchanged, only the training data differs.

## Related

- [Semantic Search](./semantic-search.md) - the retrieval step whose relevance fine-tuned embeddings are meant to sharpen.

## Sources

- [Every RAG Strategy Explained in 13 Minutes (No Fluff)](../sources/every-rag-strategy-explained-in-13-minutes-no-fluff.md) - "[0:11:04] what you can do with embedding models, just like large language models, is fine-tune them on a domain specific data set like for legal or for medical."
