RAG with Azure AI: why your retrieval strategy matters AMA

Event details

Retrieval-augmented generation (RAG) allows you to build GenAI applications that use your own data, to optimize LLM performance. Join our AMA to ask us about RAG, vector databases, running RAG...

EricStarker

Updated Feb 14, 2024

danquirk

Former Employee

Feb 14, 2024

Q2: Prompt engineering is the component of Retrieval Augmented Generation with Azure AI Search that gives you the ability to influence the formulation of output responses. The Azure OpenAI Service has content on prompt engineering (ranging from introductory to advanced) to help you with this topic: Azure OpenAI Service - Azure OpenAI | Microsoft Learn

allisonsparrow

Microsoft

Feb 14, 2024

+ 1 to Dan's comment - Fine-tuning is also a method that's effective in changing the LLMs tone/manner of speaking: "Good use cases for fine-tuning include steering the model to output content in a specific and customized style, tone, or format, or scenarios where the information needed to steer the model is too long or complex to fit into the prompt window." https://learn.microsoft.com/en-us/azure/ai-services/openai/concepts/fine-tuning-considerations

CPS
Occasional Reader
Feb 14, 2024
we are using gpt-4 model which does not support Fine Tuning (https://learn.microsoft.com/en-us/azure/ai-services/openai/how-to/fine-tuning?tabs=turbo%2Cpython&pivots=programming-language-studio)