With the emergence of generative AI, everyone wants to build an enterprise application with AI models. How do you approach knowledge retrieval in your application to improve the quality of conversations and responses, making them relevant to you?
To answer this question, we find ourselves talking a lot about vectors, vector databases, and retrieval quality. While vector search is a critical component to improve LLM-generated responses, if you want to produce the most relevant, accurate responses, you need to think beyond vectors.
I had the chance to speak with the Microsoft Mechanics crew where we talked about some of the core concepts in information retrieval and how they relate to grounding LLMs with your data. We then share practices we tested that produced the highest quality responses, including vector search, hybrid search and semantic re-ranking.
Hope you enjoy the show!
You can find the code I used in the video here. If you want to see the hard data comparing the approaches we tested, check out our post Azure AI Search: Outperforming vector search with hybrid retrieval and ranking capabilities.
For more information about Azure AI Search (formerly Azure Cognitive Search), check out the documentation here.
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