In one of my older writings, I argued that “asking the right question” was the golden key to getting quick and useful answers from people. Back then, developer communities, forums, and Telegram groups were the holy grounds where our questions lived. Everyone had Google, but the real magic was asking the right people in the right way. Ask poorly, and you’d either get mocked or dismissed with: “Dude, do your own research first.”
Today, things are different. Instead of bad-tempered humans, we now face an always-eager, sometimes confused AI. Large Language Models (LLMs) like ChatGPT have rewritten the rules of the game.
Superpowers and Weaknesses of AI
- Thrives on raw input
Feed AI code snippets, error logs, and outputs—it digests them instantly. A human would need to parse, think, and simulate first.
- Infinite patience
Humans get tired or annoyed. AI doesn’t. Throw a 3-page prompt at it; it won’t ask for a coffee break.
- Wrong context = plausible nonsense
The danger zone. Wrong or incomplete context yields answers that look right but waste hours. Context is everything.
Core Principles for AI-Era Questions
1. Research First, Then Ask AI
“Google it first” is still true. Now it means:
- Search docs, forums, or GitHub issues.
- Use AI to simplify, expand, or validate what you found.
2. Provide Maximum Context
Details matter:
- Language, version, framework
- Error messages, code snippets
- Expected output
Without this, AI can’t guess your intent.
3. Be Explicit, Define the Output Format
- Weak ask: “Explain this.”
- Strong ask: “Explain this in 3 bullet points, each with a code block and inline comments.”
The clearer your request, the more useful the answer.
4. Ask Step by Step
Instead of:
“Build me a login system.”
Try:
- Draft the flow
- Add details
- Suggest test cases
- Refine the code
Small steps prevent irrelevant dumps.
5. Request Version and Source Info
Ask directly:
- “Which PHP version does this work for?”
- “What documentation does this come from?”
This stops you from applying outdated or mismatched advice.
6. Add Safety Nets
AI isn’t flawless. For production code:
- Always ask for test scenarios
- Run everything in staging first
- Don’t skip validation
Practical Prompt Templates
Debugging
My goal:
Environment:
Code:
Error / Expected:
Expected output:
1) Explain the root cause
2) Suggest a 3-step fix
3) Add test instructions for each step
Design / Architecture
Context:
Constraints:
Problem:
Expected output:
1) 3 alternative solutions with pros/cons
2) Best option + 6-step plan
3) Risks and mitigation strategies
Concept Learning
Topic:
Level:
Expected output:
1) 3-sentence summary
2) Key concepts listed
3) Simple example
4) 2–3 reliable resources
From Human Question to AI Prompt
Original Human Question:
“X data isn’t being posted, and Y function doesn’t work. What do I do?”
AI-Optimized Prompt:
Goal: Send form data via POST to https://api.example.com/forward
Code:
Problem: Request goes out but service returns 500. Log shows ‘Undefined index: body’.
Expectation: Data should be sent, service should return 200.
Expected output:
1) List 5 possible causes
2) Steps to check each cause
3) Example fix in code
No guessing. AI now has the full picture.
Common Mistakes (and Fixes)
No: “It doesn’t work.”
Include logs, environment, and code.
No: Huge, vague asks
Break problems into smaller parts.
No: No format instructions
Say “step by step + code block.”
No: Blind trust
Always test before deploying.
Questions Still Matter
Once, asking well earned you respect in forums. Now, it earns you better AI answers.
Good prompts = good results.
AI doesn’t “know”—it reflects the quality of your input.
So treat AI like your soldier: arm it with context, precision, and clear goals. Technology changes, but the art of asking remains timeless.
Questions still matter—only the address has changed.
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