Avoid repeating $0.03 LLM queries by matching semantically similar user prompts in vector cache before hitting API endpoints.
Exact string caching (e.g. MD5(prompt)) fails when users ask the same question with different wording. Semantic caching converts incoming prompts into vector embeddings and checks cosine similarity against historical cached queries.
References & Further Reading:
Principal AI Architect. Former DeepMind Researcher specializing in Large Language Models and Prompt Engineering.