LLM Caching in Python: Choose Exact, Semantic, or Prefix Cache

About this lesson

LLM caching explained: exact, semantic, and prefix caches solve different latency and cost problems and trade off hit rate, risk, and reuse granularity. Learn practical outcomes — fewer model calls, lower tail latency, and predictable quality — with a router that picks the best strategy. Hands-on Python examples using SentenceTransformer embeddings, simulated latency, and notes on scaling with ANN indexes. Subscribe for concise AI engineering and LLM systems tutorials. #LLM #Caching #AIEngineering #Python #SemanticSearch #SentenceTransformers #Tutorials

Original Description