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AI: RAG vs CAG

 RAG vs. CAG: Solving Knowledge Gaps in AI Models - YouTube

RAG (Retrieval-Augmented Generation) and CAG (Cache-Augmented Generation) are both methods for augmenting Large Language Models (LLMs) with external knowledge. RAG dynamically retrieves relevant data for each query, while CAG preloads data into a cache for faster access. RAG is better for large, dynamic datasets and situations requiring real-time information, while CAG is suitable for smaller, more stable datasets where speed and simplicity are prioritized.

Cache-Augmented Generation (CAG) vs. Retrieval-Augmented Generation (RAG) | by Hamza Ennaffati | Medium

In contrast to on-demand retrieval, Cache-Augmented Generation (CAG) loads all relevant context into a large model’s extended context window and caches its runtime parameters. During inference, the model references this cache — no additional retrieval required.

• Pick RAG if your knowledge environment is massive, fast-moving, and you frequently need the latest information.
• Pick CAG if your domain is well-defined, stable, and you prioritize speed and simplicity (no retrieval step!).

Don’t Do RAG: When Cache-Augmented Generation is All You Need for Knowledge Tasks