RAG vs. CAG: What’s Right for Your AI Strategy?

If you’ve been scrolling through the wide world of AI-related subreddits any time in the last six or months or so, you’ve likely seen a new acronym frequently popping up: CAG. 

No, that’s not a typo from someone trying to type “RAG.” CAG, or cache augmented generation, is an alternative to RAG, or retrieval augmented generation, that bypasses the real-time retrieval that RAG requires. CAG can simplify your system complexity and eliminate the step of retrieval pipeline management, making it an appealing option for many data teams.

So, is CAG a better option than RAG for your AI development strategy? When should you use RAG vs. CAG? And why? And most importantly: How many more of these acronyms can we expect?

In this article, we’ll break down RAG vs. CAG, including the architectural components of each, their benefits and drawbacks, and which one may drive better AI performance for your data team. 

Let’s get into it.

RAG vs. CAG: What’s RAG again?

RAG, or retrieval augmented generation, is an architectural framework data organizations can use to connect a large language model (LLM) to a curated, dynamic database. By leveraging RAG, teams can improve an LLM’s outputs by allowing it to access and use the most up-to-date and reliable information available. This can include proprietary data – so long as it’s high-quality, secure, and governed effectively. 

A RAG flow can be visualized like the one below:

As you can see above, a RAG flow follows the following steps: 

• Query processing: The process begins when a user submits a query to the system. This query is the starting point for the RAG chain’s retrieval mechanism. 
• Data retrieval: Based on the query, the RAG system searches the database to find relevant data. This step involves complex algorithms to match the query with the most appropriate and contextually relevant information from the database. 
• Integration with the LLM: Once the relevant data is retrieved, it’s combined with the user’s initial query and fed into the LLM. 
• Response generation: Using the power of the LLM and the context provided by the retrieved data, the system generates a response that is not only accurate but tailored to the specific context of the query.

To effectively implement a RAG architecture, data teams must develop secure, governed data and AI pipelines to deliver usable proprietary contextual data. When this is done right with high-quality and reliable data, then RAG architecture can enable data teams to deliver more value with their AI products. 

Ok, got it. So, what’s CAG?

CAG, or cache augmented generation, loads all relevant context into a large model’s extended context window and caches its runtime parameters. Instead of an additional retrieval step during inference, the LLM can then reference the cache – meaning no retrieval pipeline management is necessary. That can be music to a busy data engineer’s ears. 

A CAG flow follows the following steps: 

• Preloading Knowledge: A curated set of documents or domain knowledge is fed into the model before any live queries.
• KV-Cache: Modern LLMs store intermediate states (the “KV cache”). CAG precomputes these states for the knowledge corpus, so they can be reused rapidly.
• Streamlined Inference: When the user asks a question, the LLM already has “everything” loaded. No separate retrieval step is necessary.

The CAG architecture flow can be visualized like this: 

When to use RAG vs. CAG?

At first glance, CAG may seem like the optimal option between the two architectures. But, there are several factors to consider when determining if your data team should leverage RAG vs. CAG. 

Benefits of RAG

RAG does provide many benefits to a data team defining their AI strategy and use cases. Benefits can include: 

• Up-to-date knowledge: RAG continuously references updated data, keeping it fresh. 
• Lighter LLM: RAG typically offloads the “memory” of knowledge to an external source, keeping the LLM lighter. 
• Reduced hallucinations: RAG references actual documents, so as long as the retrieval pipelines are high-quality, then less fact-checking may be required.

Drawbacks of RAG

RAG has many benefits, but it’s also not right for every AI strategy. There are a few reasons why a data team would not choose a RAG architecture, including: 

• Potential for high latency: Since every query requires document retrieval, RAG can be slower to produce response outputs. 
• Errors from outdated documents: If your organization doesn’t have an active data quality management strategy or data + AI observability solution, you run the risk of incorrect output retrieved from outdated or irrelevant documents. 
• Higher complexity: Maintaining an external database and keeping first-party data secure is complex, especially if data is updated frequently.

Benefits of CAG

CAG also had several benefits that data teams may deem more important. Benefits can include: 

• No retrieval overhead: Users don’t have to wait for a different search to be completed. 
• Less complexity: No retrieval pipeline and fewer moving parts make CAG simpler than RAG.
• Enhanced multi-hop reasoning: CAG processes all relevant information at the beginning via unified context.

Drawbacks of CAG

There are several reasons CAG may not be the right fit for your AI strategy, including:

• Limits to context size: If you’re dealing with a very large knowledge base, you won’t be able to load context all at once.
• More initial setup: Storing KV caches requires more compute up front. 
• Constant re-caching required: To avoid stale data, you’ll need to re-cache consistently, especially if your body of data changes frequently. 

How to Choose Between RAG vs. CAG?

Now that we know the benefits and drawbacks of each architecture type, how can you determine which is right for your AI strategy?

You should choose RAG when you’re dealing with a large knowledge base that is frequently updated or dealing with private dynamic datasets. You should also choose RAG if you have the resources to build, manage, and maintain the data quality of complex retrieval pipelines.

You should choose CAG if you’re dealing with a smaller knowledge base containing data that stays relatively consistent without needing many updates (so you don’t have to frequently re-cache). You should also choose CAG if you don’t want to build or maintain the complexity of RAG pipelines and instead want a low latency solution that can work faster. 

Whether you choose RAG vs. CAG, data quality is essential

Choosing RAG vs. CAG will ultimately come down to the components of your AI strategy. You’ll need to take a step back and assess your planned use cases, resources, and current data estate to make sure you can make an informed decision.

No matter which architecture you deem is right for your AI strategy, the most important first step will be to get your data quality in order. Without an effective data quality management strategy, your RAG, CAG, or any other AI architecture you employ will lead to outdated, unreliable, and ineffective model outputs. 

A data observability solution, like Monte Carlo, is key for gaining visibility into the health of your data and AI systems, including your RAG pipelines. When something goes wrong in the data, your team is the first to know, and you can triage and resolve the issue before it makes its way into your model’s output. 

The only way enterprise AI will work is if you can trust the data. Data + AI observability can provide that trust. Want to learn how? Speak to our team. 

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