Discernment for builders

Discernment for code

What you'll learn

Estimated time: 30 minutes

By the end of this lesson 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

(5 minutes)

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.

The Five Lenses of Discernment
Lens 1 is easy to test — run it and see. By Lens 5, you’re making judgment calls AI can’t make for you.
Lens 1
Functional Integrity
Does it work?
✓Produces correct output for real inputs, not just test data
Common AI failure
Code that passes unit tests but breaks on real data the prompt never covered
Lens 2
Production Readiness
Does it work well?
✓Handles concurrent users without race conditions
Common AI failure
Smooth in dev, broken under load or behind a real infrastructure stack
Lens 3
Problem Fit
Is it the right thing?
✓Solves the user’s actual need, not just the literal spec you wrote
Common AI failure
Technically complete feature that addresses the prompt but misses the underlying need
Lens 4
Experience Quality
Is it good?
✓Users can complete the core task without help or instruction
Common AI failure
Generic UI patterns that technically work but feel uninvested — confused or frustrated users
Lens 5
Responsible Impact
Is it responsible?
✓Transparent about AI’s role — doesn’t present generated content as verified fact
Common AI failure
AI-generated content presented as authoritative, or a demographic left out by default

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?

Feedback

As you progress through the course, we'd love to hear from you about how you are using concepts from the course in your work, plus any feedback you may have. Share your feedback here.

Acknowledgments and license

Copyright 2026 Anthropic. This course was developed in partnership with CodePath, building on the AI Fluency Framework developed by Prof. Rick Dakan (Ringling College of Art and Design) and Prof. Joseph Feller (University College Cork). Released under the CC BY-NC-SA 4.0 license.
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