Real-Time Data Integration
The discussion highlights the importance of integrating real-time information into large language models (LLMs), particularly through the use of embeddings. It’s noted that while LLMs struggle with structured data, they can generate queries based on schemas to retrieve necessary information. The conversation explores the potential of blending structured and unstructured data to enhance the utility of LLMs in various applications, such as ticketing systems and financial data analysis.In this clip
From this podcast
The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence)
Patterns and Middleware for LLM Applications with Kyle Roche - 659
Related Questions
Do I get it right that a Retrieval Augmented Generation (RAG) system can retrieve data in addition to its training data as discussed in the episode with Cohere co-founder Nick Frosst on building LLM apps for business in the episode MLOps for GenAI Applications // Harcharan Kabbay // #256 and the clip Evaluating LLM Responses?
Do I get it right that a Retrieval Augmented Generation (RAG) system can retrieve data in addition to its training data, as discussed in the episode with Cohere co-founder Nick Frosst on building LLM apps for business?
Do I get it right that a Retrieval Augmented Generation (RAG) system can retrieve data in addition to its training data as discussed in the episode with Cohere co-founder Nick Frosst on building LLM apps for business?