RAG Explained

1. What is retrieval-augmented generation?

RAG is an AI framework for retrieving facts from an external knowledge base to ground large language models (LLMs) on the most accurate, up-to-date information and to give users insight into LLMs' generative process.

1.1 Motivation

Problem: Large language models can be inconsistent.

Sometimes they nail the answer to questions, other times they regurgitate random facts from their training data. If they occasionally sound like they have no idea what they’re saying, it’s because they don’t.

LLMs know how words relate statistically, but not what they mean.

Solution: RAG can improve of the quality of LLM-generated responses.

Implementing RAG in an LLM-based question answering system ensures that the model has access to the most current, reliable facts, and that users have access to the model’s sources, ensuring that its claims can be checked for accuracy and ultimately trusted.

1.2 Explanation

2. Application of RAG

Question and answer chatbots: Incorporating LLMs with chatbots allows them to automatically derive more accurate answers from company documents and knowledge bases. Chatbots are used to automate customer support and website lead follow-up to answer questions and resolve issues quickly.
Search augmentation: Incorporating LLMs with search engines that augment search results with LLM-generated answers can better answer informational queries and make it easier for users to find the information they need to do their jobs.
Knowledge engine — ask questions on your data (e.g., HR, compliance documents): Company data can be used as context for LLMs and allow employees to get answers to their questions easily, including HR questions related to benefits and policies and security and compliance questions.

3. What are the benefits of RAG?

Providing up-to-date and accurate responses: RAG ensures that the response of an LLM is not based solely on static, stale training data. Rather, the model uses up-to-date external data sources to provide responses.
Reducing inaccurate responses, or hallucinations: By grounding the LLM model's output on relevant, external knowledge, RAG attempts to mitigate the risk of responding with incorrect or fabricated information (also known as hallucinations). Outputs can include citations of original sources, allowing human verification.
Providing domain-specific, relevant responses: Using RAG, the LLM will be able to provide contextually relevant responses tailored to an organization's proprietary or domain-specific data.
Being efficient and cost-effective: Compared to other approaches to customizing LLMs with domain-specific data, RAG is simple and cost-effective. Organizations can deploy RAG without needing to customize the model. This is especially beneficial when models need to be updated frequently with new data.