RAG: Accuracy and Explainability in GenAI Applications
Thursday, Aug 7
8:30 AM - 9:30 AM
Junior Ballroom B
Description
Accuracy and explainability are critical in GenAI applications. When information from AI-integrated solutions is inaccurate, it can impact business, people’s health, financial decisions, and even legal policies, which causes cascading repercussions. Having the best data at the right time is vital.
LLMs are not able to handle this on their own, but retrieval augmented generation (RAG) can help by providing curated data as context to an LLM, guiding it to an appropriate answer. This session will explore how vector and graph RAG address the shortcomings of LLMs, explaining their shared functionality as well as some ways they handle it differently. Finally, we will see how to build a GenAI application with RAG to see these concepts in action.
LLMs are not able to handle this on their own, but retrieval augmented generation (RAG) can help by providing curated data as context to an LLM, guiding it to an appropriate answer. This session will explore how vector and graph RAG address the shortcomings of LLMs, explaining their shared functionality as well as some ways they handle it differently. Finally, we will see how to build a GenAI application with RAG to see these concepts in action.
