FDE Times: Semantic Caching for AI Agents — measure precision on labelled query pairs before you promise savings
A 6 November FDE Times review of DeepLearning.AI’s 1h24m Redis-taught course says the FDE skill is not wiring a cache: it is picking a similarity threshold from hit rate vs false-positive trade-offs on the customer’s own logs, and invalidating when prices or inventory change.
FDE Times’s 6 November books-and-courses piece covers DeepLearning.AI’s Intermediate “Semantic Caching for AI Agents” short course (about 1h24m), taught by Redis applied-AI engineers. Cosine similarity over query embeddings can skip a second model call when two phrasings mean the same thing — but a miss still pays full LLM latency plus vector-search overhead.
The review stresses the three longest videos: measuring and improving cache effectiveness, then wiring the cache into an agent. Hit rate shows savings; precision shows how many hits were wrong answers. A hypothetical 0.90 vs 0.95 threshold trade-off (3,000 vs 2,000 saved calls, 120 vs 20 false positives on 10,000 queries) is only a template — the customer’s labelled log pairs decide the number.
Redis’s own “50% or more” cost-cut claim is treated as vendor marketing. FDEs should also set short TTLs or event-driven invalidation for fast-changing data (prices, inventory), and not confuse semantic caching with prompt caching. Promise a measurement on client logs, not a percentage from a demo.
Source: fdetimes.net