Learn about the importance of Fine-tuning Embeddings for RAG applications at https://medium.com/@ailabs/fine-tuning-embeddings-for-rag-applications-272165a31b4a
Brief explanation
What if you could pre-train your embeddings to anticipate the kinds of questions your users might ask? Here’s the idea:
- Generate Question-Chunk Pairs: For each chunk of text in your dataset, generate multiple potential questions it could answer.
- Fine-Tune the Embedding Model: Train the model to pull embeddings of related questions and chunks closer together in multidimensional space while pushing unrelated ones further apart.
While this approach might seem like overfitting, it actually focuses on optimizing for generalization. It turns out, fine-tuning embeddings in this way equips the system to handle unseen queries with improved accuracy.