RAG and CAG: A new era of knowledge processing

Written by

Silas Grey

Updated on:July-15th-2025

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Overview

In the field of artificial intelligence, knowledge retrieval and processing methods have been evolving. In the past, systems based on Retrieval-Augmented Generation (RAG) have long dominated the mainstream, but now, methods based on Cache-Augmented Generation (CAG) are showing their potential to subvert tradition and gradually become the main force of the new generation of knowledge processing. So, what is the difference between RAG and CAG? Why is CAG considered the direction of the future? Let's take a deep look at these two methods, understand their respective advantages and disadvantages, and why CAG may become the trend of future knowledge processing.

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How Traditional RAG Works

The core concept of the RAG system is to retrieve data from external knowledge sources in real time and generate appropriate answers for each query. Its workflow is roughly as follows:

Database query : First, the system searches the knowledge base to retrieve documents related to the query.
Document selection : From the search results, the system selects the most relevant documents.
Information processing : Process, understand and integrate the selected document information.
Generate answers : Finally, generate a response based on the processed information.

In this way, RAG is able to provide users with answers based on real-time data, making it widely used in many intelligent systems.

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Challenges facing RAG

While RAG performs well in many applications, it is not without its flaws. Key challenges include:

1) Frequent database queries

For every user request, the RAG system needs to query the external database. For frequent request scenarios, repeated queries will significantly increase the response time, and in some cases, the accuracy and consistency of each query may not be guaranteed.

2) High latency

Since each query needs to be performed in real time, the response time of RAG is usually between 1.5 seconds and 2 seconds. This delay may have a significant impact on some applications with high real-time requirements.

3) Complex system architecture and high maintenance costs

The RAG system needs to manage complex processes such as external database connections, query logic, and document processing. This architecture not only increases the complexity of the system, but also leads to high maintenance costs.

4) Inconsistency in document selection

Each query may select different documents from the database, resulting in some inconsistency in the generated answers. Such volatility may affect users' trust in the reliability of the system.

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CAG: Next Generation Knowledge Processing

Different from traditional RAG, CAG optimizes the process of response generation by preprocessing and caching information. It not only solves some pain points of RAG, but also brings new ideas to knowledge processing.

CAG’s innovative approach

The key innovation of CAG lies in preprocessing and caching. Unlike RAG, which needs to retrieve and process data in real time, CAG processes a large amount of knowledge in advance and caches it, achieving the following advantages:

Keep pre-processed knowledge : CAG pre-processes external knowledge and stores it in the cache, avoiding real-time retrieval for each query. In this way, the system can extract existing information from the cache more quickly and generate answers quickly.
Eliminate the need for real-time search : Since the information has been pre-processed and cached, CAG no longer relies on real-time database queries. The system can directly use the knowledge in the cache to generate responses when a request is received.
Consistency and accuracy : CAG can ensure that each query gets the same answer because it relies on pre-processed and cached knowledge rather than real-time data retrieved from the database each time. This consistency greatly enhances the stability and reliability of the system.
Improved response speed : Since the real-time query link is eliminated, the response speed of CAG is greatly improved. Generally, the response time of CAG is much lower than that of RAG, which is crucial for application scenarios that require fast response.

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CAG's Advantages

1) Lower latency

CAG greatly reduces the reliance on external databases through the caching mechanism, thereby significantly reducing response latency. In some scenarios that require high-frequency queries, CAG can provide faster answer generation than RAG.

2) Simplified system architecture

Since the CAG system does not need to perform database queries every time, the system architecture is simplified. This not only reduces the cost of development and maintenance, but also makes the system more stable and efficient.

3) Enhanced consistency

Since CAG generates responses by caching existing knowledge, it can ensure that the answers to each query are consistent, which is particularly important for applications that require high accuracy and high reliability.

4) Strong scalability

As the system continues to run and optimize, CAG can continuously expand the cache library, thereby enhancing the depth and breadth of the knowledge base. This enables CAG to respond to more diverse query needs and improves the scalability of the system.

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Comparison between CAG and RAG

As can be seen from the table above, CAG has obvious advantages over RAG in many aspects, especially in terms of response time, system architecture simplification and consistency, where CAG has shown great potential.

The Future of CAG

As technology continues to advance, CAG will likely become the standard architecture for many intelligent systems and applications. Especially in areas that require processing massive amounts of data and rapid responses, such as intelligent customer service, intelligent search engines, and recommendation systems, CAG will be able to provide more efficient, accurate, and consistent services.

Not only that, CAG also provides us with more possibilities. With the development of cache technology, the CAG system will be able to support more types of knowledge formats and handle more complex query requests in the future. This will lay a solid foundation for the next step of AI development.

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Summarize

RAG and CAG are two different methods in AI knowledge processing. Although RAG has been widely used in the past few years, CAG is rapidly emerging and showing great potential with its innovative caching mechanism and simplified system architecture. With the advancement of technology, we have reason to believe that CAG will become the mainstream method of knowledge processing in the future, driving AI towards a more efficient and intelligent direction.