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Don’t Do RAG, It’s Time For CAG
Retrieval-augmented generation (RAG) has gained traction as a powerful approach for enhancing language models by integrating external knowledge sources. However, RAG introduces challenges such as retrieval latency, potential errors in document selection, and increased system complexity.
With the advent of large language models (LLMs) featuring significantly extended context windows, cache-augmented generation (CAG) bypasses real-time retrieval. It involves preloading all relevant resources into the LLM's extended context and caching its runtime parameters, especially when the documents or knowledge for retrieval are limited and manageable.
So, without further ado, let’s dive deep into this new technique.
Topics Covered
- How Does RAG Solve Issues of Context?
- Infinite Context Window
- What Does CAG Promise?
- Other Improvements
- Understanding CAG Framework
- Conclusion
How Does RAG Solve Issues of Context?
RAG is a semi-parametric type of system, where the parametric part is the Large Language Model and the rest is the non-parametric part. Combining all the different parts gives us the Semi-parametric system. LLMs…