prompt-caching

Prompt caching reduces LLM API latency and costs by storing previously generated responses and prompt prefixes. This allows identical or similar queries to be served instantly from the cache instead of triggering new API calls.

Caching prompts in RAG pipelines is best handled using multiple caching levels like prefix, full response, and semantic similarity caching. This approach efficiently matches repeated or near-duplicate prompts to minimize API calls.

Yes, semantic similarity caching can be used in interactive chat applications to match near-duplicate prompts. It identifies queries with similar meanings and serves cached responses, significantly improving throughput.

Cache invalidation for cached LLM responses ensures outdated information is cleared or updated when underlying data changes. This safety consideration prevents stale outputs in dynamic retrieval-augmented generation deployments.

You should not use prompt caching in document processing workflows when processing highly unique, non-repeating prompts. If queries rarely overlap, caching overhead outweighs latency and cost savings, making direct API calls more efficient.