The Complete Guide to Choosing Embedding Models for RAG Applications 🚀

Author(s): MahendraMedapati

Originally published on Towards AI.

The $10,000 Question That Could Make or Break Your AI App

Picture this: You’ve just launched your company’s new AI-powered customer support system. It cost $50,000 to build, took six months to develop, and everyone’s excited about the possibilities. But there’s a problem — users are getting terrible answers. Questions about “refund policies” return information about “product warranties,” and searches for “technical support” somehow pull up marketing brochures.

The article emphasizes the critical role of embedding models in Retrieval-Augmented Generation (RAG) applications, warning that a poor choice can lead to significant failures in AI performance. It delves into the reasons why many implementations fall short, highlighting the importance of context, dimensionality, and domain alignment. It also contrasts dense and sparse embeddings, discusses optimal model selection strategies, and addresses common pitfalls. The article concludes by suggesting immediate actions to take for ensuring successful deployment and ongoing performance monitoring of embedding models.

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Published via Towards AI