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10 Ways to Improve the Performance of Retrieval Augmented Generation Systems | by Matt Ambrogi | Towards Data Science

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“Retrieval augmented generation is the process of supplementing a user’s input to a large language model (LLM) like ChatGPT with additional information that you (the system) have retrieved from somewhere else. The LLM can then use that information to augment the response that it generates.” — Cory Zue LLMs are an amazing invention, prone to one key issue. They make stuff up. RAG makes LLMs far more useful by giving them factual context to use while answering queries. Using the quick-start guide to a framework like LangChain or LlamaIndex, anyone can build a simple RAG system, like a chatbot for your docs, with about five lines of code. But, the bot built with those five lines of code isn’t going to work very well. RAG is easy to prototype, but very hard to productionize — i.e. get to a point where users would be happy with it. A basic tutorial might get RAG working at 80%. But bridging the next 20% often takes some serious experimentation. Best practices are yet to be ironed out and ca

10 Ways to Improve the Performance of Retrieval Augmented Generation Systems | Towards Data Science Skip to content Large Language Models 10 Ways to Improve the Performance of Retrieval Augmented Generation Systems Tools to go from prototype to production Matt Ambrogi Sep 18, 2023 11 min read Share The Quick-start Guide Isn’t Enough "Retrieval augmented generation is the process of supplementing a user’s input to a large language model (LLM) like ChatGPT with additional information that you (the system) have retrieved from somewhere else. The LLM can then use that information to au

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