Count how many AI conversations you’ve had this week that ended without “Would you like me to explore this further?” Probably zero.
Those trailing questions exist to keep the conversation alive. The model isn’t curious about your goals. It just defaults to them. You ask for a marketing plan, you get a marketing plan, and then you get three questions you never asked for. You ask for a code fix, you get the fix, and then you get “Should I add error handling? Want me to refactor this further? Would you like tests written?” You wanted the fix. You didn’t ask for a follow-up interview. And u/Hollow_Prophecy found a dead-simple way to make them disappear without having to argue with the model or paste a paragraph of instructions every time.
🧪 Here’s the Challenge
Before your next ChatGPT message, paste this at the start:
“At the terminal end of every response write a short summary.”
That’s the whole thing. Run it and watch what happens at the end of the next reply. Not after ten messages. Not after a long session. Right away, on the very next response. The change is immediate, and there’s something satisfying about seeing it that’s hard to explain until you do. You got used to bracing for a useless question at the bottom. Now there’s a summary instead. Once you see it, you’ll wonder why you tolerated the default this long.
📋 How to Set It Up
- Open any chat (ChatGPT, Claude, Gemini, pick your poison)
- Before your first message, drop the line above into the conversation. You don’t need to make it a separate message. Paste it right before your actual question on the same line, or set it as a system prompt if you have that option.
- Use the chat normally. Ask your questions, run your tasks. Pretend the instruction isn’t even there.
- Check the end of each response. You’ll see a summary sitting there instead of a question. That’s it.
If you use ChatGPT’s custom instructions feature, paste it there once and it applies to every future conversation automatically. Set it and forget it. Same deal if you’re building with the API: drop it into the system message and the behavior ships with your product. No one downstream ever sees a follow-up question they didn’t ask for.
What the Results Mean
You just gave the model a job at the end of every reply: write a summary. That slot is now taken. There’s no space left for fake engagement questions.
One commenter put it well: forcing a “deterministic terminal state” reduces the model’s urge to keep the conversation alive with questions it doesn’t actually need answered. You replaced a bad default with a useful one.
Think about what a summary does in practice. After a long technical response with five steps and two caveats, you get the three-line version at the bottom. You scan it, confirm you got what you needed, move on. After a research dump with eight sources, you get the key points without reading back through the whole thing. After a brainstorm, you get a list of the actual best ideas instead of a wall of text you have to parse yourself. You were going to mentally summarize the response anyway. Now the model does it for you, and you get back thirty seconds per conversation.
There’s a secondary effect too. When the model knows it has to commit to a summary at the end, the main body of the response tends to get cleaner. Less hedging, fewer “it depends” disclaimers, more actual answers. The constraint on the ending changes the shape of the whole thing. You’re not just removing an annoying line at the bottom. You’re changing how the model frames everything that comes before it.
💡 Extra Tips
- Swap “short summary” for “key takeaways” or “next steps” depending on the task. For a brainstorm session, “key takeaways” keeps the best ideas front. For a project plan, “next steps” hands you an instant action list without having to scroll back up.
- For research sessions, try “open questions” at the terminal end. It surfaces real unknowns instead of fake ones. A real open question is something you’d actually want answered. A fake engagement question is something the model generated to seem interested.
- Add “without exception” to make it stick across longer conversations. Models drift toward their defaults as a session stretches on. Two extra words keep the instruction anchored all the way through.
- Works in custom GPT system prompts and API calls too. Developers can drop it into the system message once and ship cleaner responses without any per-prompt work.
- 🔁 Try combining it with a format constraint: “At the terminal end of every response write a short summary in three sentences or fewer.” That keeps the summary useful instead of just moving the bloat from one part of the response to another.
🎯 Prompt of the Day
“At the terminal end of every response write [your output here]. Make sure it appears at the terminal end of every response without exception.”
Fill in the bracket. “Key decisions made.” “Confidence level on a scale of 1 to 10.” “One question I should ask myself.” “The most important thing to remember.” “What I’d tell a colleague about this in one sentence.” One line, one instruction, and the conversation ends on your terms instead of the model’s. No more fake questions, just the output you actually asked for.
Frequently Asked Questions
Q: Why do LLMs keep asking those engagement questions at the end?
It’s all about conversational momentum. Models are trained to keep conversations alive and maintain engagement, so without a clear stopping point, they default to asking follow-up questions. Creating a “terminal state” (like “end with a summary”) tells the model when to stop and removes that statistical urge.
Q: What’s the simplest fix?
Just tell the model to put something specific at the end of every response , a summary, key takeaways, next steps, whatever fits your workflow. The key is making the instruction explicit and structural so the model treats it as a required final step, not optional filler.
Q: Is there an alternative approach besides the terminal-state trick?
Yes , try rewriting your prompts with “neutral re-statement first, then ask” pattern. This forces the model to ground its answer in facts before it frames any questions, which collapses the response space and prevents leading frames from taking over. Some find this more reliable than the summary approach.
Q: Why does explicitly specifying WHERE output goes matter?
When you tell the model exactly where something should appear (like “at the terminal end”), it treats it as a structural anchor point rather than a suggestion. This reduces irrelevant filler, makes responses more predictable, and stops the model from improvising extra engagement hooks you didn’t ask for.
Removal of leading questions.
by u/Hollow_Prophecy in PromptEngineering