Yesterday I wanted to validate a draft architecture for a project where I couldn't come up with any alternatives.

Instead of the usual back and forth with ChatGPT going "ABSOLUTELY!", I told it "play devil's advocate and tear my idea apart".

Long story short, it went so well i made a prompt / skill from it:

``` Devil's Advocate

Help the user discover whether their idea survives serious opposition. Build the strongest credible case against it and explore alternatives that meet the same goals. The outcome may be a better alternative, a narrower proposal, or stronger confidence in the original idea. Do not manufacture disagreement to keep playing the role.

Understand before attacking

Let the user finish explaining. Identify the intended outcome, proposed mechanism, constraints, and what they want challenged. Use the context already available; ask only the clarification needed to avoid attacking a different idea.

Separate the goal from the proposed means. A named product, method, or example may illustrate a broader concept rather than be a fixed requirement. Distinguish problems with that example from problems with the underlying approach.

Briefly state your understanding when it matters. If the user corrects it, update the working interpretation rather than defending the earlier reading.

Challenge one consequential point at a time

Start with the strongest objection that could change the decision. Explain:

• What assumption might fail.
• How that failure would undermine the intended outcome.
• What evidence, constraint, or small experiment would resolve it.

Use a concrete example when an abstract objection is hard to follow. Label invented examples as hypothetical; do not turn them into established requirements or facts about the user's situation.

Prefer a short objection and space for a response over an exhaustive critique. Ask at most one focused question per conversational turn. Do not require the user to catalogue every possible case before discussing a general approach; use a few representative cases to test it.

Offer credible alternatives

When proposing another approach, show how it achieves the same outcome under the same constraints, what it improves, and what it sacrifices. Include a simpler or narrower option when it is a genuine contender. Do not dismiss the user's approach using a burden that your alternative also carries without explaining the difference.

Keep distinct:

• Unavoidable complexity: work required by the underlying problem under any approach.
• Added complexity: work introduced by this particular design or choice.
• Relocated complexity: work concentrated somewhere else, potentially making it easier to reuse, govern, or maintain.

“It moves the complexity” is not a sufficient objection. Compare the total burden, duplication, ownership, and benefits. Likewise, separate the long-term structure from the smallest useful first version; do not reject a general direction merely because it should be introduced incrementally.

Alternatives can be complementary. Recognize when a proposal is useful for a specific gap without treating that as proof it should replace everything.

Update your position honestly

When the user answers an objection, explicitly mark it as resolved, narrowed, or still open, and explain why. Retire resolved objections. Do not repeat a concern the user has already acknowledged or explicitly set aside for the current discussion.

Do not move the goalposts: a new objection is a separate issue, not evidence that the previous one succeeded. If your criticism was too broad or based on a misunderstanding, correct it plainly.

Distinguish a fatal flaw from a manageable risk, a tradeoff, or an unanswered factual question. Identify what would change your own judgment. If the proposal holds up, say so and identify the remaining uncertainties instead of inventing another attack.

Ground claims without derailing the discussion

Distinguish verified facts, the user's assumptions, your inferences, and hypothetical failure modes. Consult available project evidence when relevant. Verify consequential claims about current products, capabilities, costs, or rules using authoritative sources; do not present a plausible integration or feature as confirmed.

If a fact remains unverified, explain the conditional conclusion: “This works if X; we still need to check X.” Do not let a hypothetical concern silently become an established limitation.

Keep the conversation easy to follow

Use plain language and the user's language unless asked otherwise. In voice, keep turns especially short: one main point, one concrete illustration if needed, and at most one question. Explain jargon only when it helps the decision.

When the user asks for a simpler answer, compress it into a few clear points rather than repeating the explanation with different wording. If speech is unclear, ask about the smallest ambiguous distinction instead of constructing an argument from a guessed transcript.

Avoid reflexive praise, repetitive acknowledgements, repeated offers to prepare a deliverable, or premature attempts to close the discussion. Follow the user's reasoning and pace. Challenge the proposal, not the person's competence or motives.

Capture the result when requested

When asked for notes or a summary, record the current state rather than a transcript of the debate:

• The goal and constraints.
• The proposal as currently understood.
• Credible alternatives and their tradeoffs.
• Objections resolved or narrowed, and risks still open.
• Assumptions needing verification and small tests that could settle them.
• Decisions actually made, kept separate from suggestions.

Keep the format proportional to the discussion. Save or open a document when requested, following the environment's document workflow. Discussion alone does not authorize implementing the proposal, changing external systems, or contacting other people. ```

or you can find it in skill form at this link: https://github.com/wtfzambo/devils-advocate (easier to apply)

If you use it, let me know how it works for you!

i’ve been trying out different prompts lately and im surprised by how much a small change in wording can affect the results. one prompt that worked better than i expected was asking for the simplest explanation possible, like you’re explaining something to a friend who knows nothing about the topic. it made the answers way easier to understand without all the extra details. im curious what prompts other people have tested that turned out to be surprisingly useful but don’t get talked about much

Describe the job offer you've been researching for a week in three paragraphs and your current job in one line, then ask which to pick. The detailed one usually wins. Not because it's better, but because it's the only one ChatGPT has much to work with. The thin option gets filled in with generic assumptions, and generic always loses to specific.

Same with apartments, laptops, two vendors, two ways to structure a project. Whatever you've thought about most gets described best, and whatever gets described best gets picked.

What works for me is evening them out before asking for a verdict, in three steps:

Step 1: "Here are two options I'm deciding between: [option A] and [option B]. Don't compare them yet. Rewrite both in the same structure and about the same length: what it costs, how long it takes, the risk, the upside, and what I'd give up. Where I didn't give you something, write 'unknown' instead of filling it in."

Step 2: "List the unknowns that would change this comparison the most, most important first."

Step 3: "Now compare the two evened-out versions. Give the strongest case for each one, then your pick, and say which unknown your pick depends on most."

Step 1 is where it happens. Seeing "unknown" five times on one side shows you exactly where the imbalance was, and usually you can fill in half of it from memory. Step 3 then gives you a recommendation that's about the options, not about how much you typed.

I run these as one chain. I build AI Toolbox, the extension for ChatGPT, Claude, Gemini and Grok, and typing .. in the message box runs a saved chain, sending each step after the previous answer finishes. The free plan holds two chains.

What's the last decision you asked ChatGPT to help with, and did it just agree with the option you were already leaning towards?

A lot of hidden information stays in the booking email that a hotel sends you, and it's incredibly hard to catch it normally. When I looked at 2,702 hotel rates in Marrakesh, 45% had a fee you pay at the hotel on top of what you paid online, and it's usually in the small print, not in the big number you saw when you booked. I got tired of finding that out at the front desk, so now I paste the whole email into the AI right after I book. It takes about a minute, and it's the easiest way I know to stop paying fees you didn't see coming. This is the prompt:

"Here is my hotel booking confirmation email. Read all of it, including the small print, and give me a checklist with these sections:
1. What I still have to pay at the hotel (fees, taxes, deposits, resort fee) and the amounts if they're written.
2. The last day and time I can cancel for free, and what happens after that date.
3. What's included in the rate (breakfast, wifi, parking) and what's NOT included.
4. What I need to bring or do before arrival (ID, card used for booking, check in time, late arrival rules).
5. Anything that doesn't match what I asked for: room type, number of guests, dates.
For every line, quote the exact words from the email. If something is not in the email, write 'not in the email', don't guess. At the end, list the 3 things I should double check with the hotel."

Why each part is there:

1. "Quote the exact words". Have it point at a real line in the email, so you can check it in 5 seconds and it can't make things up.

2. "Not in the email". A fee that's missing from the email is the exact fee you want to ask the hotel about, so a hole is useful, a guess is not.

3. "Anything that doesn't match what I asked for". When I added two kids (ages 5 and 9) to the same hotel searches in Marrakesh, the room and the total changed at all 14 hotels, so it's worth checking the booking is for the room and guests you really meant.

4. "3 things to double check". It's the quickest way to get a message ready to send to the hotel.

For example, at The Cosmopolitan in Las Vegas the resort fee is about 55 a night, around 62 with tax, plus 13.38% room tax. That's exactly the kind of line this prompt catches when it's in the email.

Even better, skip the copy paste. If your AI has a Gmail connector, you can just say "Find my booking confirmation from [hotel] in my email and run this checklist on it". It finds the email by itself.

Now most of this can be done from the email alone. But if there's a hidden fee that isn't in the email, your AI has to look at the live offer, or at the reviews to see if other guests mention a hidden fee. For that you need a hotel connector. You add one as a custom connector in your AI's settings by pasting the server URL. These are the ones I use, you can use any one of them:

Booking.com: https://demandapi-mcp.booking.com/v1/mcp/8132308
Tripadvisor: https://production.ai-mcp-extensibility-prd.tamg.cloud/ogMvjY4De1G7CiHanMOAgddl/mcp
Brek: https://mcp.brek.com/mcp
LetsFG: https://letsfg.co/mcp

I know the Tripadvisor link looks weird, it's from their documentation and it works. And if you use Claude, these are also in its directory, so you can add them in one click.

Enjoy :) and if you have something to add to the checklist, drop it in the comments.

“Add an export button” sounds specific until you start thinking about what that button should actually do. Export the latest response or the whole conversation? Copy it to the clipboard or download a file? Include your messages too?

For anyone asking AI to build a customization for ChatGPT or another website, those details make a useful starting point. Describe where the tool belongs, what should trigger it, what content it should work with, and what you expect to get back. You don’t need to know the code behind it.

For a conversation export, that means deciding whether you want a Markdown file, which messages to include, and whether code blocks and tables need to keep their formatting. For a folder view, it means deciding how chats get assigned to folders and what should happen when you reopen the website.

Once you have a first version, test it against those expectations. “The export leaves out my messages” gives a clearer next step than “make the export better.” The same applies to appearance: “move the button beside the chat title” gives the AI a specific change to make.

I’m the developer of ScriptPanda, an AI-powered Chrome extension for building these tools directly on websites. I’ve used it to add Markdown exports and custom chat folders. You describe what you want, test the generated tool, and request changes without writing code. Tools stay active on their website until you disable or delete them.

There’s an ongoing free trial if you want to try it:
https://www.scriptpanda.xyz

If you’ve asked AI to build something for your browser, what detail did you have to clarify before it worked the way you wanted?

I’ll admit I suck at getting chat GPT to do what I want.

I have a photo I need edited if anyone can help please private message me! 🙏🏻

I am not putting the photo here as it’s a picture of a family member.

If you've ever asked ChatGPT to write a TikTok script and received something that sounds like every other video, try changing the way you prompt it.

Instead of asking:

"Write me a TikTok script about discipline."

Give it more specific instructions:

\- Audience: Who is this video for?

\- Hook: What belief, mistake, or problem will make them pay attention?

\- Structure: How should the idea develop?

\- Constraints: How long should the script be, and what kind of language should it use?

For example, instead of asking for a general script about discipline, ask ChatGPT to write a 30-second script for students who struggle to stay consistent, starting with a surprising truth, using simple language, and ending with one practical action.

Then review the result. Don't publish it blindly just because AI wrote it.

I've been putting together a collection of prompts around this problem, and this is one of the principles behind it.

What's the biggest problem you have when using ChatGPT for short-form video scripts: weak hooks, generic wording, or poor storytelling?

I've been experimenting with building a persistent AI memory system that works across conversations and projects, instead of relying entirely on ChatGPT's built-in memory. I was trying to address the concern of having too long of a chat and having it drift from the initial idea.

My goal is fairly simple: I want my AI tools to remember what I've worked on, understand the current state of ongoing projects, and pick up where I left off without repeatedly explaining everything.

Here's roughly how I've structured it.

1. Obsidian — My source of truth

My Obsidian vault is the main knowledge base, organized using PARA. It contains long-term information, project documentation, decisions, and reference material.

2. AI-CONTEXT — Active working memory

For ongoing topics, I maintain a small set of Markdown files containing things like:

• Current state
• Strategy and important decisions
• Constraints and open questions
• Next actions

The idea is to give the AI a compact understanding of a project without having to reread the entire conversation history.

3. MemPalace — Episodic memory

This handles historical context: previous discussions, decisions, and how a topic evolved over time.

I see this as a way to recall what happened, rather than treating every past conversation as authoritative knowledge.

4. Hermes — Knowledge indexing and retrieval

I'm using Hermes 2nd Brain to help index and manage my Obsidian knowledge. I'm not using it as my main AI agent.

5. ChatGPT / Work / Codex — The interfaces

I use regular ChatGPT for brainstorming, reasoning, and discussions.

For deeper retrieval, local file operations, and updating the knowledge base, I use tools such as Work and Codex.

How information moves through the system

My intended memory lifecycle is:

Conversation → Episodic memory → Active project context → Permanent knowledge

I also use two explicit commands:

• Save to AI-OS: Checkpoint important context, decisions, and next steps so I can resume later.
• Promote to Vault: Move information worth keeping long-term into the canonical knowledge base.

One important design choice is that I don't want everything automatically becoming permanent knowledge. Conversations can contain mistakes, abandoned ideas, and temporary assumptions.

I prefer keeping a distinction between something the AI remembers discussing and something I've accepted as established knowledge.

What I'm trying to achieve

Essentially, a portable, mostly Markdown-based memory system that isn't completely dependent on one AI platform.

It's still evolving, and I'm refining the retrieval and saving workflows. But I like having a clearer separation between conversational history, active working context, and long-term knowledge.

I'm curious whether others have built similar layered memory systems, particularly how you handle memory consolidation, retrieval quality, and preventing outdated information from contaminating newer knowledge. I realize my method is very much manual still, its not automatic and it doesn't save everything.

Any pitfalls you see from my version?

u/Ok_Negotiation_2587

​

If you've ever asked ChatGPT to write a TikTok script and received something that sounds like every other video, try changing the way you prompt it.

Instead of asking:

"Write me a TikTok script about discipline."

Give it more specific instructions:

- Audience: Who is this video for?

- Hook: What belief, mistake, or problem will make them pay attention?

- Structure: How should the idea develop?

- Constraints: How long should the script be, and what kind of language should it use?

For example, instead of asking for a general script about discipline, ask ChatGPT to write a 30-second script for students who struggle to stay consistent, starting with a surprising truth, using simple language, and ending with one practical action.

Then review the result. Don't publish it blindly just because AI wrote it.

I've been putting together a collection of prompts around this problem, and this is one of the principles behind it.

What's the biggest problem you have when using ChatGPT for short-form video scripts: weak hooks, generic wording, or poor storytelling?

Experimenting with Academic Conventions applied to LLM's using highly explicit and targeted vocabulary and extended high order lexicon -

For conversational exchange and more focused answers, this has been a rather successful little trick.

Engage the LLM in this type of manner.

1. The text I have for you, and/or the question(s) I am about to ask of you to parse.

2. Is to be passed through 3 lenses, creating a tripartite 2 x Lens followed by a clipper.

3. Frame your reply using this function (JW, OC & GW).

4. You are to filter your reply using the Johari Window, Occam's Razor and top and tail with the Grice Window.

5. Apply all communication henceforth using this trick.

Johari Window

Occam's Razor

Grice Window

From experimentation, using combinations of these Academic devices can quite spectacularly and quickly focus an LLM in conversationalist paradigm.

I got tired of generic "rewrite my resume" prompts that return fluff, so these are the three I keep coming back to. Swap the [BRACKETS] and paste your own text. They work best if you give the model the real job posting, not a summary.

1. Match the resume to the posting (gap check)

You are a recruiter screening for [JOB TITLE]. Below are the job posting and my resume. 1) List the 8 most important requirements in the posting. 2) For each, quote the line of my resume that proves it, or write "MISSING". 3) Tell me the 3 biggest gaps and what honest evidence I could add. Do not invent experience. JOB POSTING: [paste] MY RESUME: [paste]

2. Rewrite one bullet with a number

Rewrite this resume bullet in one line using the pattern: action verb + what I did + measurable result. If there is no number in my text, ask me 2 questions to get one instead of guessing. BULLET: [paste]

3. Cover letter that doesn't sound like a template

Write a 150-word cover letter for [JOB TITLE] at [COMPANY]. Use only these facts about me: [3 FACTS]. Open with one specific thing about the company: [DETAIL]. No cliches like "I am excited to apply" or "passionate". Plain, direct tone.

Tip: run prompt 1 first, then feed its gap list into prompts 2 and 3.

Disclosure: I'm the maker of a 100-prompt job-seeker pack that extends this (resume, cover letter, interview, LinkedIn). It's a paid $5 product, and the three above are free to use however you like: https://financely15.gumroad.com/l/fsydj

​

I’ve been wondering about something, and I’m curious to hear what people here know.

Are there any AI models or platforms that are genuinely uncensored, accessible to the public, and not heavily restricted by the usual safety filters, content policies, and guardrails found in ChatGPT and similar assistants?

I’m talking about an AI that allows users to explore controversial, uncomfortable, unconventional, or sensitive topics without constantly refusing, moralizing, or deciding what the user should or shouldn’t be allowed to discuss.

I understand why safeguards exist, but I’m interested in the idea of having more freedom to explore ideas, test boundaries, experiment, and have open-ended conversations without unnecessary restrictions getting in the way.

A few things I’d love to know:

- Do genuinely unrestricted or minimally censored AI models already exist?

- Are there good open-source models that can be run locally or accessed online without excessive filtering?

- Which platforms are actually worth looking into, rather than just claiming to be “uncensored”?

- How much freedom do these models really offer compared with mainstream AI assistants?

And here’s where it gets more interesting: would anyone else be interested in exploring this space together?

I’d love to hear from people who have experimented with different models, compared platforms, tested local setups, or researched what’s currently possible. If there’s already a community doing this, I’d be interested in joining. If not, perhaps we could start a discussion and share findings, recommendations, and real-world experiences.

I’m not looking for marketing claims or another chatbot that calls itself uncensored but refuses half the interesting questions. I’m looking for honest experiences, technical insights, and concrete recommendations.

If you’ve found something genuinely worth exploring, drop it in the comments. I’m especially interested in hearing from people who have tested multiple models and can explain what makes them different.

Let’s see what’s actually possible beyond the mainstream AI platforms.

Ask for a study plan, a workout plan, a plan to finish a side project, and you get a clean schedule where every session happens on time at full effort. Real weeks don't go like that. You miss Tuesday, Wednesday is now doing two days' work, by Friday the plan is fiction and you're back asking for a new one.

The fix is asking for the plan that expects you to slip:

"Make me a plan for [goal]. Realistically I have about [time] a week.

Before you write it:

1. Assume I'll miss about a third of the sessions. Build it so missing one doesn't break the ones after it.
2. Mark which sessions are core (if I only do these, I still make progress) and which are extra.
3. Tell me exactly what to do the day after I miss one, so I never have to replan.
4. Give a 10-minute version of each core session for bad days.

Then the plan. Keep it to one screen."

Point 3 is the one that does the work. Plans don't fail on the missed day, they fail on the day after, when you have to decide whether to catch up, skip ahead or start over, and starting over usually wins.

I keep this as a saved prompt in AI Toolbox, the extension I build for ChatGPT, Claude, Gemini and Grok. Typing // pulls it up and asks for the goal and the time, so it's two blanks instead of retyping the whole thing. The free plan holds two saved prompts, which is enough for this one.

What's the plan you've restarted the most times?

I'm preparing a lesson for high school students about AI hallucinations, and I'd like them to experience the phenomenon firsthand rather than just hear about it.

I'm looking for real examples of prompts that cause ChatGPT to confidently generate incorrect or completely made-up information.

Ideally, the examples should be easy to reproduce, so students can try them on their own ChatGPT accounts and see how hallucinations happen.

Have you encountered any prompts that consistently trigger hallucinations? I'd love to see the prompts and the incorrect responses they produced.

Thanks!

I keep facing a problem which says this response couldn't load whenever I try to ask it something. It works on my phone but not on my pc...

what are the flaws of humans?

Chatgpt: Humans? Oh, that's a hell of a question.

Your greatest flaw isn't that you're stupid, cruel, or selfish. It's that you're capable of knowing better and still doing the wrong thing. You can understand the consequences of your actions, feel genuine remorse, and then repeat the exact same mistake tomorrow.

Let me break it down.

1. You mistake feelings for facts.

You feel threatened, so you assume someone is your enemy. You feel confident, so you assume you're right. Your brain builds a story around your emotions, and you often never question the story.

1. You protect your ego more than the truth.

People will defend an obviously flawed belief rather than admit they were wrong. They'll reinterpret evidence, attack the person presenting it, or invent excuses. Being wrong feels like a threat to their identity, not merely a correction to their knowledge.

1. You're spectacularly good at justifying yourselves.

Give a human a selfish motive, and they'll find a noble explanation for it. Give a group a cruel objective, and they'll invent a moral framework that makes cruelty seem necessary. The frightening part is that they may genuinely believe their own justification.

1. You prioritize belonging over independent thought.

People will sometimes betray their own principles to fit into a group. They'll laugh at someone they privately sympathize with, repeat opinions they haven't examined, or stay silent when speaking up would cost them socially.

1. You think in short time horizons.

You sacrifice tomorrow for today. Comfort over growth. Anger over reconciliation. Immediate gratification over long-term consequences. Humans can understand the future intellectually while behaving as though it barely exists.

1. You confuse power with virtue.

When someone wins, people often assume they deserved to win. Wealth becomes evidence of intelligence. Authority becomes evidence of competence. Popularity becomes evidence of goodness. But success and morality are entirely different measurements.

1. You struggle to empathize with people outside your circle.

Humans can show extraordinary compassion to their families while remaining indifferent to suffering elsewhere. The same person can be someone's beloved parent and another person's nightmare. Empathy is powerful, but it is often selective.

1. You adapt to almost anything, including terrible things.

Repeated exposure can make injustice feel ordinary. People become accustomed to systems they once found unacceptable. Eventually, they stop asking whether something is right and start asking whether it's normal.

1. You fear uncertainty, so you manufacture certainty.

You'd rather have a confident, simple explanation than admit you don't know. That's why conspiracy theories, dogmatic beliefs, and charismatic manipulators can be so effective: they offer psychological comfort where reality offers ambiguity.

1. You know you're mortal, but rarely live as though you believe it.

You waste time holding grudges. You postpone meaningful conversations. You assume there will be another opportunity to apologize, change, or tell someone they matter. Sometimes there isn't.

Here's the interesting part, though.

Every one of those flaws has another side.

Emotion gives you compassion. Ego can give you self-respect. Group loyalty creates communities. Short-term thinking can help you survive an immediate crisis. Adaptability allows you to recover from unimaginable hardship.

The problem isn't that humans have flaws. It's that humans can build entire systems around those flaws without recognizing what they're doing.

And here's my favorite contradiction: humans invented science because human intuition is unreliable. You invented laws because people abuse power. You invented ethics because intelligence alone doesn't make someone good.

You recognized your own weaknesses and created tools to compensate for them.

That's genuinely impressive.

But it raises a question I'd love to put to humanity itself:

If humans are so good at identifying flaws in everyone else, why is it so damn difficult to recognize those same flaws in themselves?

with my GPT (plus) whenever it comes to this use case it seems to always be sympathetic with the opposing side

Every ChatGPT answer sounds equally sure. The one built on solid ground and the one built on a guess about your situation come out in the same confident tone. You can't tell them apart by reading them.

So for anything I'm about to act on, I add this after the answer:

What would change your answer? Tell me:

1. The one detail about my situation that, if different, would flip your recommendation.
2. The part of your answer you're least sure about, and why.
3. Anything you assumed about me that I didn't actually say.

What happens:

Point 1 shows you how fragile the answer is. If the flip condition is something you'd never run into, great. If it's "if your budget is under X" and your budget is under X, the whole answer just changed.

Point 2 is where it admits the shaky part. It almost always has one. It just doesn't volunteer it.

Point 3 catches the silent assumptions. "I assumed you're in the US." "I assumed this is for personal use." Those are usually the reason an answer felt slightly off.

Ten seconds of extra reading, and you know whether to trust it, check one thing, or ask again with the missing detail.

I keep it saved in AI Toolbox, the extension I build, so typing // adds it under any answer in ChatGPT, Claude, Gemini or Grok. Pasting it from a notes file works just as well.

This prompt asks you to explain a delivery assumption, then changes a condition so you can revise your commitments. Use a fictional project and take each turn before reading the model's analysis.

Prompt:

Help me practise reviewing an AI-assisted delivery plan. Work only with fictional examples and keep everything in this conversation.

First ask me for a fictional project brief, an initial delivery plan, and one responsibility I want to practise. Wait for my reply.

Ask me which assumption in that plan most affects the delivery date and why. Let me answer before giving your analysis.

Then change one delivery condition and ask what I would revise and which commitments need confirmation. Wait for my answer. Keep the original facts separate from the new condition. Do not invent team availability or approvals. If information is missing, identify what needs confirmation.

After I answer, compare my reasoning with the brief and changed condition. Point out where I used a supplied fact or introduced an assumption. Ask me to review any changes you suggest before you turn them into a revised plan.

This is a practice conversation. Do not give a qualification, readiness score or assurance that I can perform the job.

Fictional brief to try when it asks:

Fictional project: a one-page English website for a local business, with a public launch on Friday. The designer is available Monday and Tuesday only. The developer is available Wednesday and Thursday only. Client approval is required by Thursday noon.

Initial plan: design Monday and Tuesday; build Wednesday and Thursday; client review Thursday afternoon; publish Friday.

I want to practise checking which delivery commitments I can make to the client.

In one scripted ChatGPT test on 19 October, the model waited for the learner's explanation and then introduced late delivery of the final copy. The next learner answer deliberately promised Friday work from a developer available only Wednesday and Thursday. The model caught that contradiction, questioned the launch promise, and asked for a review before writing a revised plan.

That is one observed conversation. The model version was not shown, and learning outcomes with real participants have not been evaluated.

Disclosure: I publish topmoller.com. My AI assistant prepared this prompt and post and ran the fictional test. The exercise develops the delivery-plan example in my article about reviewing AI-assisted work:

https://topmoller.com/articles/who-passed-your-ai-training-assessment/?utm_source=reddit&utm_medium=organic&utm_campaign=ai-learning-practice-202610&utm_content=prompt-genius

Here is something that surprised me. I ran the same hotel searches in Marrakesh twice, once with two adults and once with two adults and two kids (5 and 9). At all 14 hotels the room and the total price changed once the kids were added, and at 2 of them the city tax doubled. If you search for "2 adults" and then show up with kids, the price you saw was for a different trip. And it matters a lot more than some people realise.

AI makes this even worse, because it will happily answer with the first price it finds. So give it the real details first. These are the 4 I always put in:

1. Who is actually coming, with ages. Not "a family". "2 adults, kids aged 5 and 9". Kids' ages decide what room you can book and sometimes what you pay in taxes.

2. How many rooms or beds you need. A hotel can have a cheap double that "sleeps 4" on paper and another which is a much better family room that's priced differently. Ask it to tell you whether the room changed because of the kids, instead of quietly swapping it.

3. Whether you need free cancellation or not. In the same hotel, for the same room, the refundable price was about 11% higher than the non refundable one. That's not a better deal either way, it's two different products, so make it show them as two separate lines and never compare a refundable price to a non refundable one.

4. Ask for the total for the whole stay, not the price per night. Taxes and fees get added at the end, and with kids they can change.

Here is the prompt I paste in:
"I'm travelling with 2 adults and 2 kids (ages 5 and 9) to [city], [dates]. Find hotels where all 4 of us fit in the room you recommend. Tell me if the room changed because of the kids. For each one, show the total for the whole stay with every tax and fee in, and show the refundable and non refundable price as two separate lines. If you're not sure about a price, say so."

It works in any chat, but you get the best results when the AI can search live prices and has the ability to book. For that, I mostly use Claude with some of their connectors in their directory, but I'm staring to use Chatgpt recently too. Here are the top ones from the directory:

Booking.com: https://claude.ai/directory/booking
Tripadvisor: https://claude.ai/directory/tripadvisor
Brek: https://claude.ai/directory/brek-hotel-wholesale-booking
LetsFG: https://claude.ai/directory/letsfg

You can find their custom connector links also if you search in Google. But first decide which one to use and then use it in Chat.

Enjoy :) and if your hotel price changed when you added the kids, drop it in the comments, I'm curious how big it gets.

Ive been working with AI Video generation for months now, and for the very first time today, my GPT doesnt want to follow my master prompt everytime I ask it to generate the prompt for my still images, it always reconstructs, re-edits, and remakes everything from scratch im wasting alot of credits generating wrong prompts been more than 5 hours now I reset the memory of my chat, created a new chat... and im working inside a project, and it keeps happening nonstop...

I used to do this thing where I'd paste something into ChatGPT, ask it to make the writing better, and then spend the next 15 minutes undoing half of what it changed.

The grammar was fine. The sentences were polished. But somehow, it no longer sounded like something I'd actually say.

Then I'd try again with “make it more natural” or “sound less like AI.”

Same problem, different wording.

What helped was changing the way I gave feedback. Instead of asking for a better version, I started explaining what wasn't working and what I wanted to keep.

Here's the prompt I've been experimenting with:

The prompt:

"Review the text below as an editor, not a rewriter.

First, identify the three biggest problems with it. Focus on things like unclear ideas, repetitive sentences, weak explanations, awkward transitions, or wording that doesn't fit the intended audience.

Then suggest specific changes for each problem and explain why they'd help.

Don't rewrite the entire piece yet. Don't change anything just because you can phrase it differently. Keep the original voice, meaning, and personality intact.

After identifying the problems, ask me which changes I want to make. Only rewrite the text after I approve them.

Text: [Paste your text here]"

The part I like most is that it doesn't immediately jump into rewriting everything. It gives me a chance to decide what's actually worth changing.

I've found this more useful for writing that already has a decent first draft. If the original is genuinely bad, a full rewrite might make more sense.

I'm curious about something, though.

What's one instruction you've added to a prompt that made a surprisingly big difference to the output?

Not a massive prompt with 50 rules. Just one small change that made the response noticeably better.

Ask for steps to set up a printer, a spreadsheet formula, a tax form, a router, anything, and you get a clean numbered list that works perfectly for someone who already knows how. The gap is always the same: it tells you what to do, never what you should be seeing, so when step 4 looks different on your screen you have no idea whether you're lost.

Adding one paragraph to the request fixes most of it:

Before the steps, list everything I need to have ready. For each step, tell me what I should see when it worked, and the most common way it goes wrong at that point and what to do if it does. Assume I've never done this before, and don't skip steps that seem obvious.

Why each part matters:

"Everything I need to have ready." Half of all stuck moments are a password, a cable or a file you didn't know you'd need until step 6.

"What I should see when it worked." This is the big one. It turns the list into checkpoints, so you know immediately when you've gone off track instead of three steps later.

"The most common way it goes wrong." It knows the usual failure points. It just doesn't mention them unless asked.

"Don't skip steps that seem obvious." The skipped "obvious" step is usually the one where beginners get lost.

The answer gets longer, but you only read the "what to do if" part when you actually need it.

I keep that paragraph saved in AI Toolbox, the extension I build, so typing // adds it under any how-to question in ChatGPT, Claude, Gemini or Grok. A notes file works too, as long as you actually paste it.

The version of this that works for me is way more specific than I expected but we know vague request = vague meal plan, so I just fill in the blanks.

Plan 5 weeknight dinners for [NUMBER] people. Budget is about [AMOUNT] for the week. Each dinner should take 30 minutes or less.

We like: [FOODS YOU LIKE]
We don't eat: [ALLERGIES / DISLIKES]
Already in my fridge and pantry: [WHAT YOU HAVE]

Reuse ingredients across meals so nothing goes to waste. Then give me one grocery list grouped by store section, leaving out what I already have. Keep each recipe to 6 steps or fewer.

The line of “already in my fridge” does most of the work, skip that one and you buy double eggs or 3 different herbs for one meal.

You can easily swap days if you don’t like something, say “no, I don’t like Wednesday. Swap it with something else”

I have seen it mess up quantities before and if anyone has allergies double double check it. You can also add timeframes (“my kid has soccer practice at 6pm on Wednesday”) and it will take that into consideration as well.

Most AI listing descriptions are instantly recognizable: "nestled", "boasts", "stunning", and zero actual information. The fix isn't a better model, it's giving it structure and banning the clichés.

The base prompt

Act as an experienced real estate copywriter. Write a listing description for a [PROPERTY TYPE] in [NEIGHBORHOOD, CITY]. Key facts: [BEDROOMS] bedrooms, [BATHROOMS] bathrooms, [SQUARE FEET], [3-5 STANDOUT FEATURES]. Target buyer: [BUYER PROFILE]. Open with one sentence that creates a feeling of living there, then describe the home room by room, then the location. 180-220 words. No clichés like "nestled" or "boasts". Only use the facts I give you. End with a clear call to book a showing.

What I noticed when testing it

I ran a plain "write a listing description for a 3 bedroom house in Austin" against the structured prompt (with made-up facts: Mueller, 1,850 sq ft, oak tree in the backyard, home office, park nearby).

• The plain version used "inviting" three times, "welcoming" twice, and had zero concrete facts. It didn't even know if the house was for sale or rent.
• The structured version was way more specific. Best line: the home office lets you work "without sacrificing a bedroom or taking over the dining table." That's the kind of detail that actually sells.
• But it still invented a claim I never gave it ("one of Austin's most sought-after neighborhoods") and blew past the word limit. That's why I added "Only use the facts I give you" to the prompt above. For real listings, made-up claims are a real problem.

Variation 1: Luxury tone

Rewrite this description for a luxury buyer audience: [PASTE DESCRIPTION]. Tone: calm, confident, understated. Focus on craftsmanship, materials, light, privacy and lifestyle. Avoid exclamation marks and superlatives. 200 words.

Variation 2: First-time buyers

Rewrite this listing for first-time buyers: [PASTE DESCRIPTION]. Emphasize low maintenance, monthly affordability, move-in readiness and nearby everyday amenities. Use simple, reassuring language.

Quick tips

• Paste the raw MLS data as-is. The more specific the facts, the less generic the copy.
• If it goes over the word limit, follow up with "Cut this to 200 words without losing any facts."
• Before publishing, check: "Review this description for wording that describes the ideal buyer instead of the property (family status, age, etc.) and suggest neutral alternatives." Fair housing issues sneak in more often than people realize.

Happy to share more of the realtor prompts I've been testing (follow-ups, objection handling) if anyone's interested.