Blog
Since late 2022, Large Language Model (LLM) chatbots have revolutionized AI-driven conversations using Retrieval-Augmented Generation (RAG) to pull in relevant data for context. However, latency remains a challenge in retrieving information quickly. Noetic Caching addresses this by caching contextual data rather than responses, leveraging locality principles to store frequently used or regionally relevant data closer to users. This approach, integrated by Harper, optimizes retrieval speeds, reduces reliance on distant databases, and enhances chatbot performance, offering a balance of speed, accuracy, and cost-effectiveness for better AI experiences.
Blog
Noetic Caching: The Key to Smarter, Faster Chatbots
Since late 2022, Large Language Model (LLM) chatbots have revolutionized AI-driven conversations using Retrieval-Augmented Generation (RAG) to pull in relevant data for context. However, latency remains a challenge in retrieving information quickly. Noetic Caching addresses this by caching contextual data rather than responses, leveraging locality principles to store frequently used or regionally relevant data closer to users. This approach, integrated by Harper, optimizes retrieval speeds, reduces reliance on distant databases, and enhances chatbot performance, offering a balance of speed, accuracy, and cost-effectiveness for better AI experiences.