How Azure AI Search powers RAG in ChatGPT and global scale apps

Millions of people use Azure AI Search every day without knowing it. You can enable your apps with the same search that enables retrieval-augmented generation (RAG) capabilities when you build Custom GPTs or attach files in your ChatGPT prompts.

Pablo Castro, Microsoft CVP and Distinguished Engineer Azure AI Search, joins Jeremy Chapman to share how with Azure AI Search, you can create custom applications that retrieve the most relevant information quickly and accurately, even from billions of records.

Manage massive-scale datasets while maintaining high-quality search results with ultra-compact, binary quantized vector search indexes that use Matryoshka Representation Learning (MRL) and oversampling to equal the search accuracy of vector indexes up to 96 times larger. These approaches drive significant cost savings by optimizing your vector indexes without compromising quality.

► QUICK LINKS:
00:00 - RAG powered by Azure AI Search
00:50 - Azure AI Search role in ChatGPT
02:01 - Azure AI Search use case - AT&T
03:27 - Start in Azure Portal
04:35 - Massive scale and vector index
06:08 - Scalar & Binary Quantization
07:21 - Martyoshka technique
09:07 - Oversampling
11:31 - How to build an app using Azure AI Search
13:00 - See it in action
14:28 - Enable binary quantization with oversampling
14:54 - Wrap up

► Link References

Get sample code on GitHub at https://aka.ms/SearchQuantizationSample

Check out search solutions at https://aka.ms/AzureAISearch

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