Why, When and How to Fine-Tune a Custom Embedding Model — Improve RAG Retrieval

AI Impact Summary

Customizing embedding models through fine-tuning on company-specific data is crucial for improving RAG system retrieval performance when off-the-shelf models lack domain nuance. This process involves training a smaller, domain-specific model, potentially reducing cost and latency compared to larger general-purpose alternatives. Fine-tuning leverages a base model like all-MiniLM-L6-v2, but careful consideration must be given to the evaluation pipeline and dataset preparation to ensure improvements and avoid overfitting.