đź“š Retrieval-Augmented Generation (RAG) Prompting

Course Laboratory How AI Works
1

What Is It?

Retrieval-Augmented Generation, or RAG, is a technique that enhances the abilities of large language models by combining them with external information sources. Think of it as giving the language model access to a library of knowledge that it can consult when generating responses. This approach helps the model provide more accurate, up-to-date, and detailed answers, especially for complex topics that require specific information.

In simple terms, while a standard language model generates responses based solely on the data it was trained on, RAG allows the model to “look up” relevant information from external documents or databases before crafting its response. This reduces the chances of the model making up information (a problem known as “hallucination”) and increases factual consistency.

RAG is currently the industry standard when working with LLMs in order to ensure that information is as accurate as possible.

How Does It Work?

Here’s how RAG functions step by step:

  1. Question or Prompt Received: The model receives a user’s input or question.
  2. Information Retrieval: Before generating an answer, the model searches an external knowledge source (like Wikipedia or a specialized database) to find documents or pieces of information relevant to the question.
  3. Contextualization: The retrieved information is combined with the original question. This enriched context provides the model with specific details needed to formulate an accurate response.
  4. Answer Generation: Using both the original question and the retrieved information, the model generates a response that is informed by the latest and most relevant data.

By incorporating this retrieval step, RAG-equipped models can provide answers that reflect current knowledge, even if the information wasn’t part of their original training data.

When Do You Use It?

RAG is particularly useful in situations where:

  • Up-to-Date Information Is Crucial: Topics that change frequently, like current events, recent scientific discoveries, or evolving regulations.
  • Specialized Knowledge Is Required: Fields that require detailed domain-specific information, such as law, medicine, or engineering.
  • Accuracy and Reliability Are Important: Tasks where incorrect information could have serious consequences, like medical advice or financial planning.
  • Mitigating Hallucinations: When you want to reduce the chances of the model generating plausible but incorrect information.

For example, if you’re building a customer support chatbot that needs to provide users with the latest product information, using RAG can ensure the chatbot references the most recent data.

Practical Example

Let’s see how RAG prompting works in practice with some basic examples.

In a System Prompt

System Prompt (Hidden to the User):

You are an assistant that provides answers based on the most recent information. When a user asks a question, you will first retrieve relevant documents from our knowledge base (your uploaded files) and use that information to generate your response.

Implementation:

Before the assistant responds, it searches the knowledge base for documents related to the user’s question. These documents are added to the context the assistant uses to formulate the answer.

In a User Prompt

User Prompt:

“Can you tell me the latest COVID-19 travel restrictions for international flights to Canada?”

Process:

  1. Retrieval: The assistant searches the latest government and airline regulations regarding COVID-19 travel restrictions to Canada.
  2. Generation: Using the retrieved information, the assistant crafts a response that outlines the current restrictions, such as vaccination requirements, testing protocols, and quarantine measures.

Assistant’s Response:

“As of September 2023, international travelers flying to Canada must be fully vaccinated against COVID-19. Accepted vaccines include those approved by the World Health Organization. Travelers are also required to present a negative COVID-19 test taken within 72 hours before departure. Upon arrival, random testing may occur, and unvaccinated travelers may need to quarantine for 14 days. Please check with your airline and the official Government of Canada website for the most up-to-date information before your trip.”

In this example, the assistant doesn’t rely solely on pre-existing knowledge. Instead, it retrieves the latest information to provide an accurate and current answer.

By using RAG prompting, you can enhance the performance of language models in providing reliable and precise information, making them more valuable tools for tasks that depend on factual accuracy and up-to-date knowledge.