prompt-caching

LLM API cost and latency reduction is achieved by implementing multi-level caching strategies, including prefix caching, response memoization, and Cache Augmented Generation to bypass redundant token processing and retrieval steps.

Cache Augmented Generation (CAG) optimizes LLM interactions by pre-caching document context directly, bypassing expensive Retrieval-Augmented Generation (RAG) steps to deliver faster and more cost-effective responses.

Prefix caching for repeated prompt structures is implemented through structured prompt management that leverages native caching mechanisms to reduce token consumption for identical system prompts.

Yes, you can cache LLM responses for semantically similar queries using response memoization, which stores and retrieves full LLM outputs to avoid generating identical answers multiple times.

Cache invalidation logic maintains response accuracy by systematically clearing or updating stored prompt prefixes and cached responses when underlying data or document context changes.

You should not use prompt caching for highly dynamic or unique queries where prompt structures rarely repeat, as the overhead of structured prompt management and cache invalidation logic outweighs the token reduction benefits.