Large language models (LLMs) have revolutionized the world of technology, offering powerful capabilities for text analysis, language translation, and chatbot interactions. While the promise and value of LLMs are apparent, they also have limitations that should be considered and addressed to improve their performance and efficiency. 

In this webinar, AI experts from cnvrg.io and MinIO will explore some of the shortcomings of LLMs and how you can overcome them by leveraging RAG (Retrieval Augmented Generation) and MinIO running on cnvrg.io. You’ll learn how to ensure up-to-date LLM responses and how to use RAG to improve precision, recall and contextual understanding of your LLM application. You’ll also learn strategies to create more efficient computation and reduce latency of your LLM.

You will then get a hands-on demonstration of how to build your own LLM pipeline leveraging the capabilities of RAG and MinIO, on cnvrg.io. This solution leverages RAG as an endpoint on cnvrg.io, allowing users to ask questions using API requests, cnvrg.io as the orchestration platform, and MinIO as the storage solution. 

What you’ll learn: 

• Best practices for implementing LLMs into your business
• How to improve precision and contextual understanding of your LLM application
• How to reduce LLM training costs with RAG
• How to avoid hallucinations in LLMs 
• How to ensure updated responses for your solution