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Why AI Exposes Poor Documentation Instead of Fixing It
Everyone is asking how AI can improve documentation.
I believe the better question is: What is AI revealing about the quality of our documentation?
Poorly governed content, fragmented knowledge, outdated information, and undocumented decisions don't disappear with AI—they become more visible.
In this article, I explore why documentation is no longer just a writing discipline but a strategic capability for enterprises adopting AI.
📖 Read the full article on LinkedIn:
https://www.linkedin.com/pulse/why-ai-exposes-poor-documentation-instead-fixing-amol-redij-jwwgf/
#EnterpriseAI #ArtificialIntelligence #KnowledgeManagement #TechnicalWriting #ContentStrategy #AIStrategy
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Why AI Exposes Poor Documentation Instead of Fixing It
Every enterprise technology leader right now is asking the same question: “How do we leverage AI for documentation?” They’re perhaps asking the wrong question. The real insight—the one that separates strategic thinking from vendor hype—is this: AI doesn’t fix documentation problems.
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Of course, AI now can do more than basic understanding of rough sketches or shapes, it is far beyond what the public frontier AI models we are able to access can even do, we are far too limited from the use if its full power, no complaints from me though, we should strive to support AI safety-measures to avoid catastrophe.
What do you guys use AI for?
How do you explain a technical workflow without making the user work to understand it?
For this section, I used a simple visual flow to communicate the journey from ingesting data to transforming and storing it.
The goal was to make a complex concept feel approachable at a glance.
What’s your go-to approach for simplifying technical products?
AI can write the code. But who checks the code?
One thing I’m learning as a developer:
Writing code is only half the job.
You can use AI to generate a feature in minutes.
But then comes the real work:
→ Does it actually work?
→ Does it break something else?
→ Is the data being saved correctly?
→ What happens with unexpected input?
→ Does the API return what we think it returns?
→ Can the user actually complete the workflow?
That’s where debugging becomes more than just fixing red errors.
It becomes understanding what the system is actually doing.
I’m becoming more intentional about this in my own projects:
Build → Test → Break → Investigate → Fix → Verify → Repeat.
AI makes development faster.
But I still believe the developer needs to understand why the code works—not just that the code exists.
What do you think is harder:
Building a feature from scratch, or debugging a feature that “looks like it should work”?
