engineering · 1 min read
Semantic Caching: Cutting RAG Latency and Cost by 40%+
Semantic caches return cached answers for paraphrased questions. Here is how Bhogar AI implements semantic caching with consistency guarantees.
BABhogar AI TeamProduct & Engineering
Most RAG queries are slight paraphrases of previous queries. A semantic cache that recognises this returns a cached answer in milliseconds for free, instead of paying for retrieval and generation again.
Why it matters
The risk is staleness - cached answers can outlive the underlying knowledge. The right design is short TTLs, KB-version-bound cache keys and confidence thresholds for cache hits.
How Bhogar AI approaches it
Bhogar AI offers semantic caching as a per-KB toggle, with cache keys bound to the underlying KB version. Cache TTL and similarity threshold are tunable; cache hit rate appears in the same dashboard as retrieval metrics.
- Per-KB semantic cache with version-bound keys
- Tunable TTL and similarity threshold
- Cache hit-rate telemetry
- Bypass on user-supplied "no cache" hint
- Privacy-aware caching with per-tenant partitioning
What you get
Customers enabling semantic caching cut RAG latency 40-60% on cache hits and reduce LLM spend by 25-45% overall.