Retrieval Augmented Generation

Retrieval augmented generation (RAG) is a popular way to ground prompt requests to large language models (LLMs). Grounding adds domain-specific knowledge or customer information to the prompt, giving the LLM context to respond more accurately to a question or task.

 To break it down, RAG:

• Retrieves relevant information from a knowledge store containing structured and unstructured content.
• Augments the prompt by combining this information with the original prompt.
• With the augmented prompt, the LLM generates a response.

Many LLMs are trained generally across the Internet on static and publicly available content. RAG adds domain-specific information to help LLMs give you better responses to your prompts. With RAG, you can extract valuable information from all sorts of content, such as service replies, cases, knowledge articles, conversation transcripts, RFP (request for proposal) responses, emails, meeting notes, frequently asked questions (FAQs), and more.