Differences Between RAG and CAG
Differences Between RAG and CAG
In this tutorial, let’s understand the difference between RAG and CAG.
Retrieval-Augmented Generation (RAG)
Retrieval-Augmented Generation (RAG) is a technique used in AI models to improve response accuracy by retrieving relevant information from external sources before generating an answer. Instead of relying solely on pre-trained knowledge, a RAG model searches a database, retrieves relevant documents, and incorporates that information into the response. This approach enhances factual correctness and keeps the model up-to-date.
Context-Augmented Generation (CAG)
Context-Augmented Generation (CAG) is an approach where additional contextual information is provided to an AI model before generating a response. Unlike RAG, CAG does not perform external retrieval; instead, it enriches the input prompt with relevant context, such as historical conversation data, predefined knowledge, or structured input. This technique is useful in chatbots, content generation, and personalized AI assistants.
RAG vs CAG
Some of the differences between RAG and CAG are as follows:
| Retrieval-Augmented Generation (RAG) | Context-Augmented Generation (CAG) | |
|---|---|---|
| Source of Information | Retrieves data from external sources (databases, documents, APIs). | Uses pre-supplied context within the input prompt. |
| Dependency on External Data | Depends on an external knowledge base or search system. | Does not require external data retrieval. |
| Use Cases | Fact-checking, research assistance, answering questions with up-to-date data. | Chatbots, personalized recommendations, structured content generation. |
| Accuracy and Reliability | More accurate due to real-time retrieval. | Accuracy depends on the quality of the given context. |
Examples
Example of RAG
Imagine an AI assistant for medical professionals. Instead of generating answers only from pre-trained knowledge, it retrieves the latest medical research papers and then provides an informed response.
Example of CAG
A customer service chatbot for an e-commerce site can use past conversation history and user preferences to provide personalized responses without retrieving new data from external sources.
