RAG - Retrieval Augmented Generation AI

Courtesy: Databricks.com

Retrieval augmented generation, or RAG, is an architectural approach that can improve the efficacy of large language model (LLM) applications by leveraging custom data. This is done by retrieving data/documents relevant to a question or task and providing them as context for the LLM. RAG has shown success in support chatbots and Q&A systems that need to maintain up-to-date information or access domain-specific knowledge.

RAG is the right place to start, being easy and possibly entirely sufficient for some use cases. Fine-tuning is most appropriate in a different situation, when one wants the LLM's behavior to change, or to learn a different "language." These are not mutually exclusive. As a future step, it's possible to consider fine-tuning a model to better understand domain language and the desired output form — and also use RAG to improve the quality and relevance of the response.

When I want to customize my LLM with data, what are all the options and which method is the best (prompt engineering vs. RAG vs. fine-tune vs. pretrain)?

There are four architectural patterns to consider when customizing an LLM application with your organization's data. These techniques are outlined below and are not mutually exclusive. Rather, they can (and should) be combined to take advantage of the strengths of each.