Large language models (LLMs) face a significant knowledge limitation – they cannot access information not included in their training data. This includes recent events like Oscar winners or proprietary data such as customer purchase history. To address this limitation, two key augmented generation techniques have emerged: Retrieval Augmented Generation (RAG) and Cache Augmented Generation (CAG).
Understanding Retrieval Augmented Generation (RAG)
RAG solves the knowledge problem by connecting LLMs to external, searchable knowledge bases. When a query is received, RAG retrieves relevant document portions to provide context before generating an answer. The system operates in two distinct phases:
Offline Phase:
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Documents are broken into manageable chunks
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Vector embeddings are created using an embedding model
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These embeddings are stored in a vector database
Online Phase:
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User prompts are converted into vectors
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Similarity search identifies relevant document chunks
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Top results are added to the LLM’s context window
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The LLM generates an answer using both the query and retrieved context
A key advantage of RAG is its modularity, allowing components to be swapped based on specific needs.
Exploring Cache Augmented Generation (CAG)
Unlike RAG’s on-demand approach, CAG preloads the entire knowledge base into the model’s context window at once. The process works as follows:
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All relevant knowledge is included upfront
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The LLM processes this information, creating a key-value (KV) cache
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This cache represents the model’s encoded knowledge
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Subsequent queries are added to this existing cache
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The LLM generates answers using this comprehensive context
RAG vs. CAG: Choosing the Right Approach
The primary difference between these approaches is when knowledge processing occurs:
| Feature | RAG | CAG |
|---|---|---|
| Knowledge Processing | On-demand retrieval | Preloaded entirely |
| Scalability | Handles massive datasets | Limited by context window |
| Accuracy | Depends on retriever quality | Risks information overload |
| Latency | Higher (retrieval step required) | Lower after initial setup |
| Data Updates | Easy incremental updates | Requires full recomputation |
Accuracy Considerations
RAG’s accuracy depends heavily on the retriever’s effectiveness. A good retriever shields the LLM from irrelevant information, while CAG ensures all information is present but risks overwhelming the model.
Performance Factors
While RAG has higher latency due to the retrieval step, CAG offers lower latency after the initial cache creation. For scalability, RAG performs better with large datasets through selective retrieval, while CAG is constrained by context window limitations (typically 32,000-100,000 tokens).
Practical Application Scenarios
Scenario 1: IT Help Desk Bot
For a bot using a 200-page product manual that rarely changes, CAG is preferable since the small, static knowledge base fits within the context window.
Scenario 2: Legal Research Assistant
When searching through thousands of constantly updated legal cases, RAG is better suited due to its ability to handle massive, dynamic datasets and provide precise citations.
Scenario 3: Clinical Decision Support System
For hospital systems requiring comprehensive and accurate answers, a hybrid approach works best – combining RAG for initial retrieval and CAG for creating temporary working memory.
Key Takeaways
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RAG is ideal for large, frequently updated knowledge bases requiring citations or when working with limited resources
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CAG works best for fixed, small knowledge bases where speed and simplified deployment are priorities
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The optimal choice depends on your specific application needs, with hybrid approaches offering benefits in complex scenarios
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Both techniques significantly enhance LLM capabilities by addressing the fundamental knowledge limitation