prompt-caching

Cache LLM completions to reduce latency and API costs by applying prefix and full-response caching strategies. This reuses previous completions for repeated prompts, minimizing overall API calls.

Semantic matching for prompt caching is a strategy that identifies similar queries and reuses relevant cached prompts. This lets you serve responses to similar questions without generating new completions.

Handle cache invalidation for LLM responses by applying safe caching strategies for temperature-dependent outputs. This ensures cached completions are properly invalidated when generation parameters change.

Yes, you can use prompt caching for document QA workflows. The caching strategies apply to chat-based assistants and document QA to reduce latency and API calls for repeated prompts.

The best way to implement prefix caching is to configure prompt caching to reuse prefixes and responses in your current LLM workflow. This directly reduces latency and API calls for repeated prompt prefixes.

You should avoid full-response caching for LLM completions when working with temperature-dependent outputs. Cache invalidation strategies are required to safely handle responses when generation parameters vary.