Semantic Caching Layers for High-Performance Generative AI System
Explore how semantic caching layers improve generative AI performance by using embedding-based cache lookups to reduce costly model calls. Understand system design trade-offs, infrastructure choices, and production integration techniques that optimize accuracy, latency, and cost across AI applications.
LLM-powered support systems waste significant compute because many queries are semantically identical but phrased differently. Exact-match caching fails here, as even minor wording changes lead to cache misses and repeated expensive model calls.
Semantic caching solves this by using embeddings to match queries based on meaning instead of text, reducing cost and latency. This lesson covers how embedding-based cache lookup works, the trade-offs between accuracy, latency, and hit rates, and how to integrate it into production systems.
The following diagram contrasts the traditional caching approach with the semantic caching architecture.
With this architectural contrast in mind, let’s examine how the embedding-based lookup actually works at the system level.
Designing embedding-based cache lookup
The semantic cache operates through two distinct data paths that together form a self-populating system. Understanding each path is essential before selecting infrastructure components.
The cache write and read paths
When a query arrives and no sufficiently similar entry exists in the cache, the system follows the write path. The LLM generates a response, and simultaneously, an embedding model such as OpenAI’s text-embedding-ada-002 or an open-source alternative like Sentence-BERT computes a dense vector representation of the original query. The system then stores the embedding, the original query text, and the generated response as a tuple in a vector database.
On subsequent requests, the read path activates. The incoming query is embedded in real time, and an