A student in an AI workshop was ready to quit. Every request she typed came back wrong. Wrong tone, wrong angle, wrong everything. She was sure the model was broken.
Then she and the teacher sat down together, and the problem turned out to be much smaller. She hadn’t figured out what she wanted yet. The AI was guessing, and so was she.
That story comes from u/bywill-ai on r/PromptEngineering. It stuck with me, because I’ve done the same thing more times than I’d like to admit. So here’s the six-step method from that post, with a few of my own notes.
Why this matters 🧭
A lot of “bad AI output” is really skipped thinking. One commenter put it well: if you can’t describe the decision you want the model to help with, it fills the gap with something that sounds plausible. It’s like ordering “something good” at a restaurant and then being annoyed about your dinner.
The post lists the usual mistakes:
- Too little context. If a new coworker would need a detail to do the job, so does the model. Anthropic’s guide describes it as a brilliant but new employee who lacks context on your norms and workflows.
- Too much in one request. Ask for one thing at a time.
- Never fixing the prompt. The answer misses, and people just retype the same thing with more sighing.
- Never asking the model for help. Try: “If you were able to make this prompt better, what would you do?”
- Too many gimmicks. Rigid formulas lose to a plain English conversation with confirmation and feedback.
The 6-step method 🛠️
- Start with a plain conversation. Tell the AI what you’re trying to do, in normal words. If you’re foggy, say so. Something like: “I’m not really sure what I want to do. Let’s brainstorm this together, and give me some references.” For the student in the story, this step was most of the fix.
- Ask it to question you first. Tell it to ask you questions before it writes anything. This pulls out the details you forgot to mention, and sometimes the ones you hadn’t decided on yet.
- Use a separate AI just for writing prompts. One assistant drafts the prompt, another one does the work. The post says ChatGPT writing prompts that you test in Claude Code works fine. The prompt writer stays focused on being clear, and the worker stays focused on doing.
- Test it and be honest. Run the prompt. Look at what comes back like a picky editor, not like a proud parent.
- Close the loop. Go back to the prompt writer and tell it how it went. “You did a good job, that was exactly what I needed.” Or: “That didn’t work, let’s adjust it.” Then keep going. Most people skip this step, and it’s where the improvement happens.
- Save the winners. Ask the AI to remember the prompt, and keep your own copy as an MD file or on GitHub. Over time you end up with a library of things that actually work, instead of a vague memory of that one great prompt from last Tuesday.
Tips and tricks from the crew 💡
- Save test inputs and expected outputs next to each prompt. This one came from u/djkawh in the comments, and it’s the missing piece of a prompt library. A prompt without an example of “good” is hard to trust three months later.
- Mind your model and your plan. Another commenter pointed out that the model you use, and which paid tier you’re on, can change results a lot.
- Let the AI know who you are. One commenter has a long chat history with ChatGPT, so it already knows their work and how they like answers. They used it to write prompts for new experiments. Shared context saves a lot of typing.
- Name your files by job, not by date. “cold-email-first-draft.md” beats “prompt-final-v3.md”. You’ll find it faster, and you’ll actually reuse it.
- One ask per message. If you catch yourself writing “and also” for the third time, split it up.
A fair warning from the comments: not everyone is sold on the whole idea, and one person joked that this is coaching little league, except the players don’t listen. Fair. These tools are not perfect, and you still have to check the work.
Try it today 🚀
Pick one task you’ve been fighting with an AI. Don’t write the prompt yet. Open a chat and type: “I’m not totally sure what I want here. Ask me questions until you understand, then draft a prompt.” Answer honestly, test the result, and tell it how it went. Then save the version that worked.
The original author is curious how others handle steps 5 and 6. Do you keep a prompt library, and how do you organise it so you actually reuse it? Has a separate prompt writer worked better for you than iterating in one chat? Drop your setup in the comments, and we’ll compare notes.
Source: the full thread on r/PromptEngineering by u/bywill-ai, with a longer write-up at bywill.ai/blog/the-grammar-of-asking.
Frequently Asked Questions
Q: Do I need a separate AI just to write prompts?
Not necessarily. One commenter used ChatGPT to draft prompts for Meta’s Muse, and it worked well because the tool already knew their work and how they like responses. Another commenter prefers staying in one tool, since bouncing between a prompt-writing session and a work session adds hassle. Try both and see which saves you more back-and-forth.
Q: What do I do when a long chat starts to drift?
Long sessions get fuzzy, so one commenter saves a log after an hour or two, or once the context window is around 50 to 70 percent full. Ask the AI to write down the important session data along with a startup prompt, then paste that file into a fresh session to pick up where you left off. For bigger projects, keep a numbered open items list and a critical path that the AI updates as you go.
Q: How do I know a prompt is actually working?
A commenter recommends saving test inputs and expected outputs right beside each prompt. That turns a vague feeling that it looks right into something you can check. When a tweak quietly breaks things, you can rerun the same inputs and see exactly what changed.
Q: Is a prompt library worth the effort?
Opinions split here. Some people treat saved prompts as the core of their workflow, since it turns a pile of chats into a reusable library. Others say a formal library isn’t needed and that the feedback loop is nice to have rather than essential, though keeping old prompts to review later costs very little.
My student’s AI kept “hallucinating”. The real problem was she didn’t know what she wanted yet. The 6-step method I teach now
by u/bywill-ai in PromptEngineering