Claude Academy
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Discernment for code

Lesson 630 min
In this lessonBy the end, you’ll be able to
  • Evaluate AI-generated work across five lenses: correctness, quality, fit, experience, and responsibility
  • Raise your quality bar beyond "it works" by learning failure modes specific to AI-built products
  • Catch AI's predictable technical blind spots before they reach production

Discernment for code

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Discernment for code · 5 min

SummaryTranscript

When AI can spin up a working product in minutes, “working” stops being the bar. You’ll learn where AI-built products typically fail, the technical blind spots that surface in production but not in development, and how to build the taste that AI doesn’t have.

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Key takeaways

  • Code that runs can still fail. The default AI output is technically complete but often misses the point.
  • AI has predictable blind spots in concurrency, security, and anything that only breaks at scale.
  • Taste is a builder skill. AI delivers functional. Making it worth using is on you.

Exercise

User testing the Clinic project

Put your build in front of a partner playing a patient or clinic admin — don’t explain, don’t help. Watch where they get confused, what they ignore, and what they wanted that you never built. Write down three things you’d change and which lens each falls under.

Lesson reflection

  • Which of the five lenses do you naturally apply, and which do you have to remind yourself to check?
  • When AI produces something that isn’t good enough, what’s your instinct: fix it yourself, or describe it better?

What's next

You’ve stress-tested the Clinic Wait Time Checker through the lenses you tend to skip. Next, you’ll look at the same tool through a different lens: how does it actually feel to use?