EPISODE · Mar 18, 2025 · 13 MIN
RAG vs. CAG: Augmenting AI Model Knowledge
from Build Wiz AI Show · host Build Wiz AI
The YouTube video from IBM Technology explains two primary methods for augmenting the knowledge of large language models: Retrieval Augmented Generation (RAG) and Cache Augmented Generation (CAG). RAG involves retrieving relevant information from an external knowledge base to supplement the model's training data for a specific query. CAG, conversely, preloads the entire knowledge base into the model's context window. The video details the workings, capabilities, and trade-offs of each approach, including accuracy, latency, scalability, and data freshness. Finally, it presents hypothetical scenarios to illustrate when each method, or a hybrid approach, might be most suitable.
What this episode covers
The YouTube video from IBM Technology explains two primary methods for augmenting the knowledge of large language models: Retrieval Augmented Generation (RAG) and Cache Augmented Generation (CAG). RAG involves retrieving relevant information from an external knowledge base to supplement the model's training data for a specific query. CAG, conversely, preloads the entire knowledge base into the model's context window. The video details the workings, capabilities, and trade-offs of each approach, including accuracy, latency, scalability, and data freshness. Finally, it presents hypothetical scenarios to illustrate when each method, or a hybrid approach, might be most suitable.
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RAG vs. CAG: Augmenting AI Model Knowledge
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