RAG vs. CAG: Solving Knowledge Gaps in AI Models

Summary: The video discusses the knowledge limitations of large language models (LLMs) and introduces two augmented generation techniques—Retrieval-Augmented Generation (RAG) and Cache-Augmented Generation (CAG). RAG utilizes an external knowledge base to retrieve relevant information on demand for generating answers, while CAG loads all available knowledge into the model’s context at once. The video compares these two approaches in terms of accuracy, latency, scalability, and data freshness, highlighting when to use each method based on specific scenarios.

Keypoints:

Large language models struggle with recalling information not present in their training set.

Augmented generation techniques help LLMs overcome knowledge limitations.

Retrieval-Augmented Generation (RAG) queries an external knowledge base for relevant information.

Cache-Augmented Generation (CAG) preloads all knowledge into the model’s context for immediate access.

RAG operates in two phases: an offline phase for knowledge ingestion and an online phase for retrieval and generation.

CAG processes a “knowledge blob” in one forward pass, storing relevant information in a key-value (KV) cache.

RAG’s accuracy relies heavily on the performance of the retriever, while CAG guarantees all relevant information is available but relies on the model to extract it.

RAG can introduce latency due to the retrieval process, while CAG generally provides faster responses after the initial caching.

RAG can scale to very large datasets, while CAG has limitations based on the model’s context window size.

RAG allows for easier updates to the knowledge base, while CAG requires re-computation for any changes.

Scenarios for RAG or CAG include IT help desk bots, legal research assistants, and clinical decision support systems, each with distinct requirements for knowledge retrieval and response accuracy.

A hybrid approach combining RAG for retrieval and CAG for context memory can be beneficial for complex applications.