How can RAG be helpful for FP&A and Finance departments?

Let’s start this article by defining this key AI term: Retrieval-Augmented Generation (RAG).

An RAG, or Retrieval-Augmented Generation, is a framework used in AI, particularly in natural language processing.

It’s designed to enhance the capabilities of language models by combining two core components: a retriever and a generator.

• Retriever: This component is responsible for fetching relevant information or documents from a large dataset or corpus. The retriever searches for data that can be useful in answering a query or contributing to the generation process.
• Generator: This is typically a language model (like a version of GPT) that generates text. It uses the information retrieved by the retriever component to produce more informed, accurate, and contextually relevant responses.

How can RAG be helpful for FP&A and Finance departments?

Imagine you have a huge library of all your company’s financial reports, market data, and industry research.

Now, suppose you have a question like, “What were our sales trends over the last five years?” or “What are the projected growth rates for our industry?”

RAG is like a highly efficient librarian combined with an expert analyst.

First, it goes through all the information (like a librarian finding the right books), and retrieves the most relevant financial data and reports for your question.

Then, it acts like an finance analyst, summarizing and explaining this information in an easy-to-understand way, helping you make informed financial decisions or create accurate reports.

• Retriever -> Librarian
• Generator-> Finance Analyst

The key idea behind RAG is to augment the language model’s ability to generate text by providing it with access to a wealth of external information.

This approach can lead to more informed and accurate responses, especially for questions that require specific factual knowledge or for tasks that benefit from external references.