Semantic caching stores LLM responses and returns cached results for semantically similar queries, not just exact matches. This reduces API costs and latency by avoiding redundant LLM calls for questions with the same meaning.
How It Works
- Embed incoming queries into vectors
- Search cache for similar embeddings
- Return cached response if similarity exceeds threshold
- Otherwise, call LLM and cache the result
Benefits
- Cost reduction: Avoid redundant API calls
- Lower latency: Cache hits are instant
- Consistency: Same questions get same answers
Monitoring Cached Responses
- Track cache hit rates and quality
- Monitor for stale or mismatched responses
- Compare cached vs fresh response quality
- Adjust similarity thresholds based on metrics
Does caching affect quality?
It can. Cached responses may become stale or not perfectly match the new query's intent. Monitor cache hit quality and set appropriate similarity thresholds to balance cost savings with accuracy.
Practical example
An assistant considers reusing a response for a question with similar wording but a different customer account.
What to check
Test semantic false matches, tenant scope, source freshness, expiration, and invalidation.
Limits and tradeoffs
Similarity is insufficient when dates, permissions, negation, or account-specific facts change the correct answer.
Maintained by DriftRail. AI assisted the October 2026 content and template revision. Examples are illustrative; they are not measured product benchmarks. Suggest a correction.