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When two calls share the same opening text, the second one can reuse that prefix from cache instead of paying full price for it.

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Improvements it proposes

Weflayr looks at each feature’s real calls, measures how much of that achievable caching you already capture, and proposes two kinds of improvements to capture the rest:
  • Config changes: finds the best the caching settings based your AI calls and on each provider’s own caching rules (retention, explicit vs implicit caching, minimum cacheable length, breakpoints and so on).
  • Prompt-structure changes: reorders the prompts when it makes sense so more of it can be cached.
Open a feature to see its analysis.

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Details on prompts

View details on prompts on a finding shows the real calls it applies to, each as it ran versus with the fix, highlighting the tokens that would move into cache.
Per-example view comparing a prompt as it ran and with the caching fix applied