Retrieval-Augmented Generation (RAG) is a widely used application pattern for Large Language Models (LLMs). It uses information retrieval systems to g

Building RAG with Open-Source and Custom AI Models

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2024-05-06 08:30:04

Retrieval-Augmented Generation (RAG) is a widely used application pattern for Large Language Models (LLMs). It uses information retrieval systems to give LLMs extra context, which aids in answering user queries not covered in the LLM's training data and helps to prevent hallucinations. In this blog post, we draw from our experience working with BentoML customers to discuss:

By the end of this post, you'll learn the basics of how open-source and custom AI/ML models can be applied in building and improving RAG applications.

Implementing a simple RAG system with a text embedding model and an LLM might initially only need a few lines of Python code. However, dealing with real-world datasets and improving performance for the system require more than that.

To build a robust RAG system, you need to take into account a set of building blocks or baseline components. These elements or decisions form the foundation upon which your RAG system's performance is built.