TL;DR: A r/PromptEngineering user shared a long meta-prompt that reads your whole chat and writes the next prompt for you. It’s useful when you’re stuck mid-project. The short version of it probably does 80% of the job.
What this prompt does
You’re deep into a conversation with ChatGPT, Claude, or Gemini. There’s a lot of context, and you’re not sure what to ask next. u/liamblank wrote a prompt for exactly that moment.
You paste it into the live conversation. The model looks at everything so far, works out what’s blocking progress, and hands back one copy-ready prompt. You review it, then send it as your next message.
It can return four things:
- One next prompt
- A short sequence of dependent prompts
- One focused question for you
- A note that the task is already done
The design choices worth stealing
The full prompt is long, but the logic behind it is clear. These are the parts that change behavior:
- 🧭 Your latest goal wins. If you changed direction ten messages ago, the prompt follows the new direction, not the old one.
- 🔍 Earlier model claims aren’t evidence. The AI said something confidently and you nodded. That doesn’t make it verified. The prompt tells the model to question it.
- 🛠️ Retrieve before asking. If the model can look something up with its tools, it should do that instead of asking you.
- Stop when you’re done. It’s allowed to say the work is complete, rather than inventing more tasks to look busy.
That last one matters more than it sounds. Most AI conversations drift toward “more.” A prompt that’s allowed to say \”finished\” pushes back on that.
The shorter version
The author admits the length is the obvious concern, and offers a compact alternative to compare against. Here it is:
Review this conversation and identify what currently limits progress toward my latest stated goal. Return one copy-ready prompt that addresses it. Prefer producing the actual deliverable when possible. Question consequential assumptions rather than treating earlier model claims as verified. If you need an answer from me to choose the next action, ask one focused question instead. If the task is complete, say so. Don’t add unnecessary work.
That’s about 70 words against a few hundred. It keeps the main ideas: find the blocker, prefer real output, distrust old claims, ask only when needed, and know when to stop.
Where it can go wrong
It can lock in a bad frame. If the conversation started with the wrong question, the model will faithfully optimize the next step inside that wrong question. A meta-prompt that reads the chat inherits the chat’s mistakes. If something feels off about the whole direction, step back yourself before running it.
It’s missing a \”wait\” option. One commenter (u/NinjaConsultingLLC) pointed out that sometimes the work is ready and you’re waiting on a colleague’s approval. There’s no useful prompt, no question to answer, and the task isn’t complete. None of the four outputs fits. Adding a fifth, something like \”nothing to do until X happens,\” would close that gap.
Another commenter, u/Friendly_Boat2845, uses something similar for coding. Their take: the length matters less than you’d think if the output stays tight. The real test is whether it catches bad assumptions before you build on them.
Does the extra turn pay off?
This is the author’s third question, and it’s the right one. You’re spending a turn to choose a turn. That’s worth it when:
- The conversation is long and you’ve lost track of what’s settled
- You suspect the model made claims nobody checked
- You’re out of ideas and need a push
It’s not worth it when the next step is obvious. Then just say \”do the next thing.\”
Use cases
- Coding: You’ve built half a feature and aren’t sure whether to test, refactor, or check an assumption about an API.
- Research: You have a pile of findings and need to know which claim to verify before it goes in the report.
- Writing: A draft exists, but you can’t tell if it needs structure work or a fact check.
- Planning: The plan feels complete and you want an honest answer on whether it actually is.
Prompt of the Day
Start with the short version above. Paste it into a conversation that’s stuck, and read the prompt it gives you before sending it. If the result feels shallow, then try the full version and compare.
Your move
Run both versions on the same stalled conversation and see which next prompt moves the work further. The author is asking people to share the task, the model, the prompt it chose, and what happened next. If you test it, post your results in the thread. That’s the data everyone’s missing.
Frequently Asked Questions
Q: Is the long version worth using over a shorter prompt?
Some readers found that length matters less than you’d expect if the output stays tight, and that a shorter version gets you about 80% of the way for everyday work. Others argue the detailed version earns its keep when the stakes are high or the conversation is a mess. A simple rule: use the short one for routine next steps, and the long one when the thread has drifted or a wrong move would be expensive.
Q: Could ‘follow my latest instruction’ lock in a mistake?
One commenter worried that this rule can preserve a constraint you typed casually while tired, when the earlier, broader goal was the one that mattered. Others see your newest message as the best signal of what you want now. A practical middle ground is to have the prompt flag any conflict between your latest instruction and the original goal, then ask you one question instead of silently following either.
Q: What should the tool say when you’re waiting on someone else?
A commenter pointed out that the four outputs (prompt, question, sequence, or done) have no slot for ‘blocked.’ If a colleague’s approval is due tomorrow, the selector might invent a review pass just to fit one of the options. Adding a wait option that names the external event and the condition for resuming keeps the advice honest. Watch for the opposite problem too, where the wait option becomes an easy exit from work you could still do.
Q: How do you know it catches bad assumptions?
A commenter who built a similar tool for coding said the real test is whether it catches bad assumptions before they compound, not how long the output is. So far nobody in the thread has reported results from a multi-day research thread where the goal drifted. If you try it on a long project, keep a log of the assumptions it flagged and check how many turned out to matter.
I wrote a meta-prompt to answer “What should I ask the AI to do next?” Looking for critique.
by u/liamblank in PromptEngineering