An adaptive caching architecture for LLM systems that uses a lightweight model to dynamically tailor cached responses to similar user queries. By integrating semantic caching with Milvus as a vector database, the system reduces response generation latency and cuts compute costs by up to 68% on real-world user-LLM interaction datasets.
@article{cheema2025tweakllm,title={TweakLLM: A Routing Architecture for Dynamic Tailoring of Cached Responses},author={Cheema, Muhammad Taha and others},journal={arXiv preprint arXiv:2507.23674},year={2025},url={https://arxiv.org/abs/2507.23674},}