One signal

A developer who built and tested a semantic cache for large language model calls concluded that most systems should not use one. The reported problem: prompts that look highly similar can still have opposite or unsafe meanings, and a small verification model may not reliably catch the difference.

Why it matters

The cache used DynamoDB and an additional model to check whether paraphrased prompts could share an answer. The author says the tests exposed an error risk that can be difficult to distinguish from ordinary model mistakes. That is a useful warning for anyone weighing faster or cheaper LLM calls against the cost of serving the wrong answer with confidence.

Discuss: Would you accept a small risk of wrong cached answers for lower cost and latency, or should semantic caches be avoided unless they can prove they catch dangerous near-misses?

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