AI tools have changed how engineers at WellSky write code, draft documentation, and explore unfamiliar parts of our systems. On a good day, they help us move faster. But the real shift isn't in how much code we can produce — it's in the skill that now matters most: knowing when to challenge what the model just told you.

A confident wrong answer is still wrong.

AI assistants produce fluent, plausible output, and that fluency is exactly what makes uncritical acceptance risky. A function that looks idiomatic might call an API that doesn't exist. A summary that reads cleanly might omit the detail that mattered most. The model isn't evaluating whether a response is correct — it's generating a response that sounds plausible. Recognizing the difference between what sounds right and what is right remains the responsibility of the engineer.

On our teams, we think of this as a habit rather than a checklist. A few practices have proven helpful:

  • Read before you accept. Review every AI-generated change as if a teammate wrote it. If you wouldn't approve it from a person, don't approve it from a model.  
  • Ask the model to challenge itself. A simple prompt such as "What could be wrong with this approach?" can surface edge cases the first answer overlooked.  
  • Verify what's cited. When an AI tool references a library, function, or existing pattern, confirm that it exists and behaves as described.  
  • Own the output. When code lands in a pull request, the engineer who opened it is accountable — regardless of where the draft originated.  

‍

None of this slows engineers down the way some teams expect. In fact, skepticism is what allows us to move faster with confidence, because the work we ship is genuinely ours.

The most valuable engineers in an AI-assisted world aren't the ones who use these tools the most. They're the ones who use them thoughtfully — treating each AI-generated suggestion as a starting point for judgment, not a substitute for it. That's the craft we're investing in.

Further Reading:

  1. Ardan Labs — "AI Hallucinations: When Plausible Logic Masks Context" (2026) https://www.ardanlabs.com/news/2026/ai-hallucinations-when-plausible-logic-masks-context/
  1. Birgitta Böckeler & Martin Fowler — "Exploring Gen AI" series https://martinfowler.com/articles/exploring-gen-ai.html