Semantic Caching & LLM Cost Optimization (Wakil AI)

Production research to cut RAG and LLM API costs through semantic caching — safely — for the Wakil AI platform.

Highlights

• Ran a semantic-caching study on 4,000 real production queries.
• Found the headline “55% overlap” figure misleading: most of it came from same-session follow-ups, not reusable FAQ traffic that’s safe to cache.
• Identified a safe scope (~10–15% of traffic) and designed a Layer 1 FAQ cache with strict query gates and query-only embeddings.
• Documented false-positive cases and recommended a staged rollout — turning “cache everything” into a defensible, data-grounded strategy.