What is semantic caching and when is it useful?

Semantic caching embeds incoming queries and checks if any cached query is within a similarity threshold, returning the cached answer instead of re-running the pipeline. It works well for customer support, FAQ, and documentation assistants where users ask slightly different versions of common questions. The trade-off is false positives where two semantically similar queries expect different answers, requiring careful threshold tuning. Tools like GPTCache, Redis vector search, or direct integration with a vector database implement semantic caching.