Most developers assume that once AI starts writing the code, the precision bar drops. Hand off the task, collect the output, ship it. Turns out, that assumption is exactly where things go wrong.
A short but sharp post in r/PromptEngineering puts it clearly: working with AI doesn’t eliminate engineering skill, it relocates it. The author argues that the core of the job is still very much intact, just expressed differently. And I found the framing genuinely useful because it names something a lot of people feel but can’t quite articulate.
This Redditor lays out three core skills that haven’t gone anywhere:
🎯 The Shift, Not the Loss
The old way: write precise code.
The new way: write precise instructions.
Same underlying skill, different medium. The author breaks it into three areas where that precision now lives:
- Clear, specific prompts: Vague instructions give vague results. If you wouldn’t write an ambiguous function spec, don’t write an ambiguous prompt.
- Context as a tradeoff signal: The AI can’t read your mind about constraints, priorities, or edge cases. You have to supply that context explicitly, or the AI makes its own tradeoffs, and they might not be yours.
- Defining “done” upfront: How do you validate the output? What does correct actually look like? If you can’t answer that before generating, you can’t evaluate what comes back.
None of those are new ideas. They’re core engineering practices wearing different clothes.
⚠️ The Complacency Trap
Here’s where the post’s author gets into territory most AI workflow guides skip. When AI generates all the work, it’s tempting to skim the output, assume it’s correct, and move on. That’s exactly where bugs, security issues, and subtle mistakes slip through.
Attention to detail doesn’t become less important when AI handles the heavy lifting, it becomes more important. You’re the last line of review. If you stop being a careful reader of AI output, you stop being an engineer and start being a rubber stamp.
Old way: you made the mistakes, so you knew where to look.
New way: the AI makes the mistakes, and they’re often confident, plausible, and wrong in ways you won’t catch without scrutiny.
The responsibility hasn’t transferred. Only the authorship has.
📚 Two Guides Worth Bookmarking
The post’s author points to two resources for anyone who wants to build actual prompt engineering skill rather than just vibes-based prompting:
- Anthropic’s Claude Prompting Best Practices
This is the official guide from the team that built Claude. It covers how to structure prompts for clarity, how to use system prompts effectively, how to handle multi-step tasks, and how to think about output format. Practical, well-organized, and updated regularly as the model improves. - OpenAI’s Prompt Engineering Guide
OpenAI’s equivalent, covers the same territory from a slightly different angle. Useful even if you primarily use Claude, because the underlying principles (specificity, role-setting, chain-of-thought prompting) transfer across models.
Both guides are worth reading in full, not just skimming. The details matter, which is kind of the whole point of the post.
Quick-Start Takeaways
If you want to apply the post’s core ideas right now, here’s the condensed version:
- Before prompting, write out what “correct output” looks like, treat it like a test case
- Add context that the AI can’t infer: constraints, audience, tradeoffs you care about
- Read AI output with the same skepticism you’d apply to a junior developer’s first PR
- Use the Anthropic and OpenAI guides as references, not one-time reads
Where to Go Next
Beyond the two guides, the practical next step is to start treating your prompts like code. Version them. Note what works and what doesn’t. Build a personal library of prompt patterns for the tasks you repeat most. The people getting the most out of AI tools right now aren’t the ones prompting faster, they’re the ones prompting more carefully.
The original post is short, but the discussion thread in r/PromptEngineering is worth checking if you want to see how other engineers are thinking through this shift. Search for “Engineering with AI is still engineering” and you’ll find it.
Engineering with AI is still engineering — two must-read prompt engineering guides
by u/Mitija006 in PromptEngineering