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Beyond RAG: How cache-augmented generation reduces latency, complexity for smaller workloads
As LLMs become more capable, many RAG applications can be replaced with cache-augmented generation that include documents in the prompt.
A new study by the National Chengchi University in Taiwan shows that by using long-context LLMs and caching techniques, you can create customized applications that outperform RAG pipelines. Called cache-augmented generation (CAG), this approach can be a simple and efficient replacement for RAG in enterprise settings where the knowledge corpus can fit in the model’s context window. For RAG, they combined the LLM with two retrieval systems to obtain passages relevant to the question: the basic BM25 algorithm and OpenAI embeddings.
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