How vector search and semantic ranking improve your GPT prompts

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 images
09:40 - Keyword search
11:22 - 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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#copilot #generativeai #vector #gpt