Learning what you actually want from AI tools through their failures, rather than specifying requirements hypothetically.
The Taoist sage learns by observing nature's response rather than imposing predetermined plans. Applied to AI tool adoption, this means treating early failures as information-rich rather than frustrating. When an AI assistant produces poor results, examine what gap its failure reveals: Is your request unclear? Do you need a different tool type? Is the problem actually unsolvable through automation? These failures teach what you genuinely need versus what you thought you needed. Many people spend weeks specifying perfect requirements before testing a tool, only to discover their actual needs differ. Laozi would advocate reverse-engineering your true intentions through iterative interaction with the tool itself. Ask the AI questions about its limitations. Observe where it excels and where it falters. This empirical approach builds understanding faster than theoretical planning. Your workflow requirements emerge through friction with reality, not armchair analysis. This practice also cultivates humility—discovering your initial assumptions were incomplete or misguided.
Peri can explain this concept, give practical examples, help you decide whether it applies to your situation, or recommend a journey if appropriate.
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