Lecture Notes: Retrieval Augmented Generation (RAG)

Overview

• The lecture is part of a video series rotating through educational topics, use cases, and bias/ethics/safety topics.
• Focus on what Retrieval Augmented Generation (RAG) is and its significance in AI and large language models.

What is RAG?

• RAG is a solution pattern for systems that leverage large language models using one's own content.
• It's important for creating experiences similar to ChatGPT but with proprietary or specific content.

Comparison: Traditional Search vs. Large Language Models

• Traditional Search Engines
  • Search and list relevant content from the internet.
  • Users click links and interpret data themselves.
• Large Language Models
  • Digest, combine, and generate answers from the internet's content.
  • Provide more cohesive and direct answers.
  • Can handle instructions, generating documents, lesson plans, etc.

Application on Proprietary Content

• To provide a custom experience, such as a chatbot on a website or documentation library.
• Ideal for customer service applications, resolving service tickets, etc.
• Allows for generating answers using specific content from one's organization.

RAG Architecture

• Scenario Example: Chatbot for a healthcare system.
  • Use proprietary content to answer questions like how to prepare for knee surgery or to confirm parking availability.
• Process:
  • User question is bundled into a 'prompt'.
  • The prompt is sent to a large language model for response generation.
  • Enhancements include additional instructions and relevant proprietary content.

Prompt Before the Prompt

• Instructions sent with the prompt to guide the language model's response.
• Include specific content information to craft a more accurate response.

Vectorization of Content

• Content is divided into chunks (e.g., paragraphs or pages).
• Each chunk is converted into a 'vector', a numeric representation of the essence of the paragraph.
• Similar topics result in similar vectors (close numeric representation).

Retrieval Process

• User question is also vectorized.
• A comparison is made between the question vector and content vectors to retrieve the most relevant documents.
• Top documents are selected to be part of the 'prompt before the prompt'.

Vector Database

• Stores the numeric representation of content.
• Facilitates retrieval by allowing comparison of vectors.

Conclusion

• RAG involves retrieving content and augmenting the generation process of the language model.
• This pattern is prevalent in LLM projects for creating interactive, custom experiences.
• It’s widely popular due to its effectiveness in generating relevant and accurate responses.