How I Explain Retrieval Augmented Generation (RAG) to Business Managers

(in 3 Minutes)

Large language models have been a huge hit for personal and consumer use cases. But what happens when you bring them into your business or use them for enterprise purposes? Well, you encounter a few challenges. The most significant one is the lack of domain expertise.

Remember, these large language models are trained on publicly available datasets. This means they might not possess the detailed knowledge specific to your domain or niche. Moreover, the training data won't include your Standard Operating Procedures (SOPs), records, intellectual property (IP), guidelines, or other relevant content. So, if you're considering using AI assistants "out of the box," they're going to lack much of that context, rendering them nearly useless for your specific business needs.

However, there's a solution that's becoming quite popular and has proven to be robust: RAG, or Retrieval Augmented Generation. In this approach, we add an extra step before a prompt is sent to an AI assistant. This step involves searching through a corpus of your own data—be it documents, PDFs, or transactions—to find information relevant to the user's prompt.

The information found is then added to the prompt that goes into the AI assistant, which subsequently returns the answer to the user. It turns out this is an incredibly effective way to add context for an AI assistant. Doing so also helps reduce hallucinations, which is another major concern.

Hope you find this overview helpful. Have any questions or comments? Please drop them below.

If you're a AI practitioner and believe I've overlooked something or wish to contribute to the discussion, feel free to share your insights. Many people will be watching this, and your input could greatly benefit others.