I built a demo on how to implement Cache-Augmented Generation (CAG) in an LLM and compare its performance gains to RAG (111 stars, 20 forks).
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I built a demo on how to implement Cache-Augmented Generation (CAG) in an LLM and compare its performance gains to RAG (111 stars, 20 forks).
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https://github.com/ronantakizawa/cacheaugmentedgeneration
https://github.com/ronantakizawa/cacheaugmentedgeneration
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CAG preloads document content into an LLM’s context as a precomputed key-value (KV) cache. This caching eliminates the need for real-time retrieval during inference, reducing token usage by up to 76% while maintaining answer quality. 
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CAG preloads document content into an LLM’s context as a precomputed key-value (KV) cache. This caching eliminates the need for real-time retrieval during inference, reducing token usage by up to 76% while maintaining answer quality. 
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CAG is particularly effective for constrained knowledge bases like internal documentation, FAQs, and customer support systems, where all relevant information can fit within the model's extended context window.
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CAG is particularly effective for constrained knowledge bases like internal documentation, FAQs, and customer support systems, where all relevant information can fit within the model's extended context window.
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#rag #retrievalaugmentedgeneration
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#rag #retrievalaugmentedgeneration
I built a demo on how to implement Cache-Augmented Generation (CAG) in an LLM and compare its performance gains to RAG (111 stars, 20 forks).

https://github.com/ronantakizawa/cacheaugmentedgeneration

CAG preloads document content into an LLM’s context as a precomputed key-value (KV) cache. This caching eliminates the need for real-time retrieval during inference, reducing token usage by up to 76% while maintaining answer quality. 

CAG is particularly effective for constrained knowledge bases like internal documentation, FAQs, and customer support systems, where all relevant information can fit within the model's extended context window.

#rag #retrievalaugmentedgeneration
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