Intermediate · Systems

Semantic Caching

Visual diagram · (in preparation) · Math · (in preparation) · Worked example · 3 difficulty levels.

TL;DR. A caching strategy for LLM interactions that stores and reuses responses for semantically similar prompts, reducing latency and API costs.

Technical Definition

A caching strategy for LLM interactions that stores and reuses responses for semantically similar prompts, reducing latency and API costs.

How it works

Semantic caching improves the efficiency of LLM applications by identifying prompts that convey similar meaning, even if phrased differently. Instead of sending every query to the LLM, the system first checks if a semantically close query has already been processed. If a match is found, the cached response is returned, saving computational resources and speeding up user experience.

Related Concepts

• Vector Database — A specialized database optimized for storing, indexing, and querying high-dimensional embedding vectors using similarity search.
• Embeddings — Dense vector representations of discrete items, capturing their semantic relationships and meanings.
• LLM Tooling — A suite of software tools and libraries designed to facilitate the development, deployment, and management of Large Language Models.