𝗪𝗵𝘆 𝘀𝗵𝗶𝗽𝗽𝗶𝗻𝗴 𝗳𝗮𝘀𝘁 𝘄𝗶𝘁𝗵 𝗔𝗜 𝗶𝘀 𝗮 𝘁𝗿𝗮𝗽
Shipping a feature in 20 minutes with AI is not a success. It is a sign you sped up the cheap part of your job. You skipped the expensive work.
Writing code was never the hard part of engineering. The real challenge is these tasks:
- Defining requirements
- Cutting scope
- Setting constraints
- Proving the change is correct
When you skip these steps, AI helps you ship the wrong thing faster. It creates a gap between looking done and being done. AI provides clean code, but it does not tell you if that code fits your system. It does not tell you the long term cost.
AI amplifies your existing habits. Good judgment gets faster. Bad judgment gets faster too.
Current workflows show the risk:
- People wipe production databases because AI sounded confident.
- Review burdens grow as you accept more code.
- Most bugs come from unclear requirements, not bad code generation.
These are old engineering problems in new masks. Prompting is a skill you learn in a weekend. The real skill is shaping the work through a sequence:
- Requirements
- Gap identification
- Planning
- Small changes
- Review
- Verification
Your first prompt should focus on the test that proves the work is right. Do not make it your last step.
Tools like Git or CI/CD only work when you rebuild your workflow around them. The tool matters less than the workflow.
Engineers who win will not be those who use AI the most. They will be those who slow the problem down before they speed up the code. Most people use AI, but few engineer with it.
How do you adjust your workflow to handle these verification gaps?
Source: https://dev.to/yerkerakhimov/why-shipping-fast-with-ai-is-a-trap-3f9l
Optional learning community: https://t.me/GyaanSetuAi