How vector search and semantic ranking improve your AI prompts
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Improve the information retrieval process, so you have the most optimal set of grounding data needed to generate useful AI responses. See how Azure Cognitive Search combines different search strategies out of the box and at scale - so you don’t have to.

Keyword search—match the exact words to search your grounding data

Vector search—focuses on conceptual similarity, where the app is using part of the dialogue to retrieve grounding information

Hybrid approach—combines both keyword and vector searches

Semantic ranking—to boost precision, a re-ranking step can re-score the top results using a larger deep learning ranking model

Pablo Castro, Azure AI Distinguished Engineer, shows how to improve the quality of generative AI responses using Azure Cognitive Search.

  ► QUICK LINKS: 00:00 - How to generate high-quality AI responses 01:06 - Improve quality of generative AI outputs 02:56 - Why use vectors? 04:57 - Vector Database 06:56 - Apply to real data and text 08:00 - Vectors using images09:40 - Keyword search11:22 - Hybrid retrieval 12:18 - Re-ranking 14:18 - Wrap up

  ► Link References Sample code available at https://aka.ms/MechanicsVectors) Complete Copilot sample app at https://aka.ms/EntGPTSearch) Evaluation details for relevance quality at https://aka.ms/ragrelevance)

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