Fine-tuning Embedding Models Explained | RAG for ML #12

The OpenAI embedding model has never read your internal documentation, your proprietary product names, or your industry jargon. When users search using domain-specific vocabulary the retrieval quietly underperforms. Fine-tuning your embedding model on your own data closes that gap dramatically. In this episode we cover: When general purpose embeddings are not good enough What training pairs and hard negatives are Building a fine-tuning dataset from your own documents Training with MultipleNegativesRankingLoss Evaluating before and after fine-tuning Swapping the fine-tuned model into your RAG pipeline Next up: Parent-Child Chunking