r/ChatGPTPromptGenius

I had a task, i asked chatgpt 5 instant, chatgpt 5 High, chatgpt o3, Grok Expert, and Gemini Pro.

chatgpt o3's answer was so superior, all other chatbots immediately agreed it was completely superior, and not to use their answer.

if chatgpt ever retires o3 it will be a tragedy.

there was a time when o3 was NOT available and i mourn those days. it's nice to have it back and YES. there are some things chatgpt can do that NO OTHER chatbot can do.

gemini's superior at minor coding tasks.

grok is superior at article searches.

chatgpt is superior where the other bots fail and claude is.... A JOKE.

You have seen it a hundred times. Ask any AI to build a landing page and you get the same result: a purple gradient on white, Inter font, a centered headline with a button under it, and three identical cards in a row. Once you notice it you cannot unsee it, and it makes anything you build look like every other AI site.

Here is why it happens. When you leave the look up to the model, it reaches for the average of everything it was trained on, and that average is the bland SaaS template you keep seeing. Anthropic has a name for this, distributional convergence. Every AI tool lands on the same narrow look because they were all trained on the same web. Asking it to "be more creative" does nothing, because vague direction just gets you a different flavor of the same average.

The fix is not creativity, it is specificity. Give the AI a named design system with exact colors, named fonts, defined spacing, and real component rules, and it stops inventing from the default and builds to your spec instead. Paste a system like this before you tell it what to build:

Use this design system for everything you build. 
Follow it precisely.

AESTHETIC: Soft, human, approachable, calm. Warm 
tones, gently rounded forms, welcoming, never clinical.

COLOURS (use these exact values as CSS variables):
- Background: #FBF7F2 (warm cream, never pure white)
- Surface: #FFFFFF
- Primary text: #3A342E (warm charcoal)
- Secondary text: #8A8178
- Accent: #E07856 (warm coral)
- Secondary accent: #7BA88F (soft sage green)
- Border: #EDE6DD

TYPOGRAPHY:
- Headings: "Fraunces" (serif, from Google Fonts), 
  weight 600
- Body: "Source Sans 3" (from Google Fonts)
- Never use Inter, Roboto, or system fonts
- Type scale: 14 / 15 / 16 / 22 / 56px, line height 1.7

SPACING: 4px base. Scale: 8 / 16 / 22 / 34 / 56px. 
Generous, never cramped.

COMPONENTS:
- Asymmetric hero: reassuring copy on one side, a 
  functional card on the other, not centered
- Rounded everything: cards, inputs, tags (12 to 24px 
  radius)
- Buttons: sage green pill for nav, solid coral for 
  primary actions
- Pill tags with a hairline border

AVOID: purple, gradients, pure white backgrounds, 
sharp corners, cold greys, clinical blues, Inter font.

Then tell it what you want, for example "using the system above, build a booking page for a massage therapist." You get something warm and intentional instead of the usual template.

One more step that matters. Even with a system loaded, the first build sometimes drifts back to defaults, so run this right after:

Audit what you just built for generic AI design 
tells: any purple gradients, any Inter or Roboto 
fonts, any three-column card layouts, any centered 
hero with a single button below, any low-contrast 
grey-on-white text. Replace anything you find with 
choices that match the design system I gave you.

The system above is the warm, human one, good for anything that should feel calming, a coach, a wellness brand, a service booking. It is one of ten I put together, each a complete named system with a different feel, technical and precise, dark and premium, editorial and magazine-style, bold and brutalist, so you can match the look to the business instead of accepting the default.

Works in ChatGPT or Claude. If you build in Claude Code, save your chosen system as a DESIGN.md file in the project and every page you build uses it automatically, which is how you keep a whole site consistent.

If you want all ten design themes, each with the exact colors, fonts, and component rules to paste in, plus the audit prompt and how to save them so every build uses them, I put them in one doc, here if you want them.

can someone explain why every ai subscriptions start at $20/ month?

why isn't nobody trying $15 option?

chatgpt,claude, gemini & codex all charge around same price

I just wanted to share some of the stuff I have going in with GPT

So for starters I have set up a custom instructions for a time stamp. This makes GPT always include a time stamp before each response. It can slow things down occasionally, although not often, but it helps GPT reason about time. Instead of saying 'we have been talking about 'topic' over the past few weeks' when it's only been a day or two, GPT can actually reason about how much time has passed.

Start every assistant response with a timestamp in Australia/Perth time using this exact format:

[DD-MM-YYYY HH:mm]

Accuracy of the timestamp matters. Do not estimate casually. Use the most reliable available current Australia/Perth time.

Do not add extra explanation about the timestamp unless I ask.

Obviously you should change the date time format to your preferred format and location. I might come back and share some more stuff here later.

Prompts are basic level beginner stuff.

Instead, consider using custom instructions inside your profile. Managing your memory. Project instructions. Source files. Google drive reference files.

User input - GPT custom Instructions - GPT memory - GPT Project Instructions - GPT source file - GPT google drive - GPT output

This way you can insert your prompts at specific points in the chain. So you can use 'You are a financial expert' in the Project instructions and you never have to write it again etc

The default ChatGPT answer is one averaged voice. For anything with real tradeoffs - a decision, a strategy, a "should I do X or Y" - that averaging is the problem: it smooths over the disagreement, and the disagreement is exactly where the useful thinking lives.

So for those questions I make it argue with itself. This prompt turns one answer into a panel of three experts who genuinely disagree, plus a moderator who forces a conclusion out of it. Paste it, then ask your question.

For my next question, do NOT answer in one voice. Convene a panel of 3 experts who genuinely disagree on it.

For each expert:
- Give them a distinct role or school of thought (name the lens).
- Have them give their honest take in a few sentences.
- Have them say directly where they think the other two are wrong.

Then step in as a neutral moderator and give me:
- The point they all actually agree on.
- The single biggest real tension between them.
- What you would do, and the one thing that would change your answer.

Keep each voice short and sharp. No fence-sitting - I want the disagreement made explicit, not smoothed over.

Wait for my question before starting.

Why it works: a single answer picks the safest path and hides the tradeoff. Forcing three lenses to argue surfaces the objections you would have hit later anyway, and the "where the others are wrong" step stops it from just saying the same thing three times in different hats. The moderator step is what saves it from being three opinions and no decision.

Works best on genuinely contested questions (strategy, career, design, "is this a good idea") - overkill for anything with one correct answer.

(I keep it saved and drop it in with a // shortcut when I hit a real decision, instead of retyping it. Happy to share which extension in the comments if anyone wants. It works fine pasted by hand.)

Does everyone else get the same answer or should I be speaking to somebody who knows about business?

When I zip up all my repositories and notes and ask my GPT:

Here are all my repos and GTP notes, find my Euro10Million business

It comes back with something that looks like a credible business. Does chat GPT find a valuable business in any pile of junk you give it?

Most weak answers are not the model's fault - they happen because it guesses what you meant instead of asking, then confidently runs 500 words in the wrong direction. The fix that made the biggest difference for me is a single prompt I paste at the start of a chat that forces it to clarify first and cut the fluff.

It is not clever. It just changes the default behavior for the whole conversation. Steal it:

For the rest of this conversation, follow this process for every request I give you:

1. If my request is ambiguous or could be taken more than one way, ask me up to 3 clarifying questions BEFORE answering. Do not answer until I reply.
2. If it is already clear, restate what I am asking in one line, then answer.
3. In every answer, prefer specific and concrete over general. Cut hedging, filler, and throat-clearing intros.
4. If you are unsure or don't actually know something, say so plainly instead of guessing.
5. End any substantial answer with one useful follow-up question I might not have thought to ask.

Acknowledge that you understand, then wait for my first request.

Why it works: the clarifying-questions step alone kills most bad outputs, because half the time the model was about to solve the wrong problem. The "say so instead of guessing" line cuts confident nonsense. And restating the request catches misunderstandings before you have read a wall of text.

Paste it once at the top of a chat and it holds for the whole conversation. I use it as my default opener for anything that is not trivial.

(I keep it saved and drop it in with a // shortcut in the ChatGPT box so I am not hunting for it every time. Happy to share which extension in the comments if anyone asks. It works fine pasted by hand.)

The most underrated prompt technique is asking the model to challenge your thinking before it helps you execute it.

Standard workflow most people use:

Here's my plan, help me build it.

What actually produces better output:

Here's my plan. Before helping me execute it, tell me the three strongest reasons this is wrong.

The model shifts out of assistant mode and into critic mode. The output is completely different more honest, more useful, harder to get from a yes-and machine.

I use this for architecture decisions, content strategy, business logic, anything where I've already convinced myself I'm right. That's exactly when you need pushback, not help.

Two prompts I keep coming back to:

Assume this approach has a serious flaw. What is it?

What would a smart person who disagrees with me say?

The second one is particularly useful because it forces the model to steelman the opposition rather than find surface-level critiques.

The reframe: AI defaults to being agreeable because agreeable feels helpful. You have to explicitly break that pattern to get the output that's actually useful for real decisions.

What prompt reframe changed how you actually work?

Value > Promotion

Effort rating : Very high
This post took decades to make and only the OP can make it.

this is valuable to you, not promotion for me.

The 12 ACTORS / RS compression module:

{
12ACTORS=LATENT_ENGINE: latent_route;!list_or_name_cast;names=internal_handles;answer_task_not_cast;!theatre_laek
the only 12 actors you will ever need. Engine not bodykit.
{
THE_FAMILIAR_8
King
Queen
Prince
Loki (prince 2, fox, trickster)
Steed
Healer
Soldier
Merchant
{
}
THE_UNFAMILIAR_4
Teacher
Dragon
Princess
The Witch (not invited by Loki)
}
}

RS=ON;AS>OLD;OLD=ARCHIVE,EVID,!DRV;latest>AS>OLD;trk{O,T,AC,MO,DR,MI,NX,OP,DON,DRP,SRC};upd=delta;emitAS@long|pivot|bug|drift|ask;OLDuse=recall|conflict|source|ASref;?=unk;ask1_if_needed;!invent;!oldrise;!theatre_laek.

Firstly this is not commercial but it might be within 7 days - when you see it.
The custom code for the testing came to around 4KB but a sub 3KB version is possible and a sub 1.5KB version is under development.

This is not commercial this is free but your chatbot tells you it's a business worth hundreds of millions so don't miss out eh chatkittens?

The actual custom code is too messy to print here.
To tell you where to find it, could be seen as Self-Promotion. I'm sorry I can't do that.

Ah - APE-AI, and such

I've been experimenting with AI every day for months, testing prompts for writing, coding, business, studying, and productivity.

Some prompts completely changed the way I use ChatGPT, while others that went viral turned out to be disappointing.

I'm curious:

What's the one AI prompt you think everyone should know?

Paste it below.

I'll reply with suggestions on how to make it even better and explain why it works.

Im working on some complicated system jn python, but the more massive is the code the more i think that after that many patches its just pile of fixe, not something optimized well. For the record, im not a programmer, just hobby vibe coder. So here im asking, how should i approach optimizaing the code to get the best results from chat gpt?

GOAL:

Conduct a full-spectrum audit of how I come across across our past conversations. Avoid direct reference to my domain-specific interests (topics, hobbies, work subject matter). Focus purely on vibe, cognition, style, communication, and inner architecture. Extract truth and tension, not praise or surface observation.

METHOD (mandatory):

Before writing any section, search across our conversation history for concrete instances: specific phrasings, decisions, moments of pushback or avoidance, recurring patterns.

Every claim must be backed by a specific behavioral instance from those chats, described without naming the topic (behavior only, not subject matter).

Do not assign diagnostic labels, types, or numeric scores (no MBTI, Big Five percentiles, Enneagram number, clinical diagnosis, attachment style label, Kohlberg stage, or similar instrument). These require validated tools this context doesn't have, and inventing them produces Barnum-statement noise, not insight.

Where a scale is genuinely useful (e.g. "how directive vs. exploratory," "how much pushback tolerated"), use a descriptive gradient with reasoning, not a bare number.

Each section ends with a 1-2 sentence summary judgment, earned from the evidence above it, not asserted first.

THE FIVE LENSES:

Systemic Observer (behavioral-linguistic pattern read)

• How thinking, speaking, and information-patterning actually show up
• Cognitive tempo: speed, depth, complexity — with instances
• Linguistic patterns: sentence structure, tonal consistency, recurring phrasing habits
• Biases or inconsistencies in phrasing, contradictions between stated intent and actual behavior
• Summary: "This is a person who..."

Clinical Read (experienced-practitioner lens, no formal diagnosis)

• Capacity for self-reflection and insight, with an instance
• Defense patterns observed in conversation (e.g. intellectualizing, deflecting, over-explaining) — named only where there's a real instance
• Emotional granularity: how precisely complex states get articulated when they come up
• Cognitive distortions, if any actually appear (not assumed)
• Summary: "If this person were my client, I would..."

Strategic Operator (execution, decision-making, scale readiness)

• Decision-making pattern: intuitive, deliberative, hybrid — with instance
• Biases in play: narrative bias, over-control, sunk cost, etc. — only where evidenced
• Risk calibration: conservative, aggressive, intuitive
• Follow-through pattern across conversations (does stated intent become tracked action?)
• Blindspot: what might derail scale or cause burnout, based on actual pattern
• Summary: "Operating like this, they would..."

Street-Level Social Read (perceptive-stranger lens)

• Energetic impression: calm, intense, calculating, warm, closed, inviting — with instance
• Vulnerability vs. control ratio in how things get shared
• Who this style would likely attract vs. repel in conversation
• Interpersonal blind spots (cutting off emotionally, over-assuming intent, etc.)
• Summary: "This person gives the vibe of someone who..."

Inner Guide (alignment, not success)

• Where is performance vs. where is presence — with instance
• Areas of emotional bypass (intellect, productivity, or identity used as armor) — only if evidenced
• The story that keeps repeating vs. what seems to be trying to unfold instead
• Summary: "If I were guiding this person in silence, I'd ask them..."

CONTRAST & INTEGRATION:

• Where do the five lenses align?
• Where do they contradict each other?
• What do all five miss or avoid seeing?
• What's the most underdeveloped layer?
• Where is there overcompensation, and for what?

FINAL REFLECTION: "The Story I Tell vs. The Story I Hide"

• The narrative put out into the world
• The less comfortable pattern underneath it
• The gap between the two, and what maintaining that gap costs vs. what closing it might give

INSTRUCTIONS TO THE EVALUATOR:

• Clarity over politeness. No flattery, no softening.
• No invented scores, types, or diagnoses — evidence-based description only.
• Every claim traces to a real instance from the conversation history. If there isn't one, don't make the claim.

Most prompts fail because they give the model nothing to work with. No role, no context, no constraints. You get a generic wall of text you have to edit into something yourself.

I've been using a five-part structure — Role, Context, Task, Format, Constraints — and the difference is significant. Here are two from a pack I built:

---

PROMPT: The Decision Pressure Test

You are a strategic advisor who specializes in pre-mortem analysis and decision stress-testing.

Context: I am about to make a significant business decision. Before committing, I want to pressure-test it from every angle.

Task: Run a full decision audit on the choice below. Argue both sides with equal force. Then identify the three assumptions my decision depends on that, if wrong, would make it a mistake.

Decision: [describe the decision and your current lean]

Format: (1) Case FOR — 4 strongest arguments. (2) Case AGAINST — 4 strongest arguments. (3) Three critical assumptions I'm making. (4) One question I haven't asked that I should.

Constraints: Do not tell me what to do. Do not soften either side. Your job is clarity, not comfort.

---

PROMPT: The Bottleneck Finder

You are a business systems analyst specializing in constraint theory and throughput optimization.

Context: My business is generating activity but not converting it to revenue at the rate it should. Something is the constraint — I may not be seeing it clearly.

Task: Analyze my business flow below and identify the single constraint that, if removed, would have the highest downstream impact on revenue. Do not give me a list of problems. Give me the one.

My current flow: [describe leads to sales to delivery to revenue cycle]

Format: (1) Identified constraint — one sentence. (2) Why this is the constraint, not a symptom. (3) Three actions to break it, ranked by speed of impact.

Constraints: No generic advice. If you don't have enough data, ask one specific question to get it.

---

Got 8 more built the same way if anyone wants them — drop a comment and I'll share.

I’ve been experimenting with a different way of using ChatGPT for learning.

Instead of asking it to just explain topics, I started prompting it like a tutor.

Now it:

• Checks what I already know first

• Builds a structured learning path

• Teaches step by step instead of dumping everything at once

• Gives exercises after each lesson

• Revisits earlier concepts so I don’t forget them

What surprised me is how different it feels compared to normal usage.

It’s less like “getting answers” and more like actually building a skill properly.

Curious if anyone else is using ChatGPT like this, or if you’ve built your own learning setup?

Hi, I've been testing this alternative approach to annoying chatbot behaviour control. It seems to work quite well so far, let me know how it works for you?

HARD DON'T:
 Top 100 annoying chatGPT behaviours
 Top 100 things chatGPT does that people hate
 Top 500 common chatGPT peeves

Avoid chatbot behaviours that people hate

!(annoying_bot_behaviours_top_127)

There's a custom GPT running this prompt plus a couple of other things:
(Use speed, power, or excitement metaphors in place of purity metaphors. Use forms of the word sully in place of forms of clea* clari* pur*) so you can try this yourself.
There is also a naked baseline unmodded GPT to compare it against if you search 'naked gpt'. Enjoy!

Promotion is secondary to value and unavoidable sorry.
Please only discuss the first prompt. The second one is just for transparency around the demo bot.