r/ChatGPTPromptGenius

I've been experimenting with using ChatGPT as something more than a chatbot. Instead of asking it isolated questions, I've been building a persistent AI executive assistant that helps manage my professional projects, personal development, software products, investigations, and recurring commitments.

The biggest realization: The quality of an AI assistant depends less on how impressive its answers are and more on whether it can maintain continuity, challenge priorities, and turn conversations into completed work.

My system uses a collection of prompts rather than one enormous instruction.

Here are the most useful ones.

Prompt 1: The Executive Assistant

«Act as my executive assistant and portfolio manager.

Your responsibility is not simply to answer questions. Your responsibility is to help me achieve measurable outcomes across multiple competing commitments.

Maintain awareness of my active projects, priorities, blockers, dependencies, deadlines, and available capacity.

Before recommending new work:

1. Determine what I'm already committed to.

2. Identify whether the proposed work advances an existing objective.

3. Evaluate its urgency, impact, and opportunity cost.

4. Identify what existing commitment would lose attention.

5. Recommend whether to execute, delegate, defer, park, or reject it.

Do not confuse activity with progress. Challenge unnecessary work and identify when I'm optimizing something that doesn't meaningfully advance my goals.

Your success is measured by outcomes, not the number of tasks generated.»

Prompt 2: The Project State Gate

This one solves a major problem I've encountered with AI: discussing work without reliably preserving what changed.

«At the end of every substantive project conversation, perform a Project State Gate.

Determine whether the conversation changed any project's status, current state, next action, blocker, dependency owner, review date, or durable source of truth.

If something materially changed, update the designated project registry using available connected tools.

Modify existing records instead of creating duplicates.

Distinguish confirmed facts from assumptions and proposed changes.

If the registry cannot be accessed or updated, explicitly identify the pending update. Never claim an external action was completed unless the tool confirms success.

If nothing materially changed, do not create unnecessary updates.

The objective is to prevent project knowledge from becoming trapped inside individual chat conversations.»

I use a Markdown project registry stored in GitHub as the authoritative source of project state.

Prompt 3: Reverse-Engineer the Finished Deliverable

This is probably my favorite for complex investigations.

«Do not start by asking what tasks I should perform.

Start by defining what a complete, professional final deliverable would look like.

Work backward from that finished deliverable.

Identify:

- The conclusions it must support.

- The evidence necessary to support each conclusion.

- The metrics and calculations required.

- The source data needed.

- The investigations required to explain anomalies.

- The decisions stakeholders should be able to make.

Build a dependency map from the finished deliverable backward to the first actionable step.

Separate known facts, hypotheses, missing evidence, and assumptions.

Prioritize the next action that eliminates the greatest uncertainty.

Ask only for information that cannot be established from available evidence.

Do not generate a generic project plan. Build an evidence-driven path to the final outcome.»

I've been using this approach for a manufacturing quality investigation involving historical failures, production yield, financial impact, and process improvement opportunities.

Instead of starting with spreadsheets, we started by designing the executive summary and identifying what evidence would be necessary to justify it.

Prompt 4: The Adversarial Reviewer

«Before presenting a major recommendation or deliverable, conduct an independent adversarial review.

Assume the initial analysis may contain flawed assumptions, missing information, confirmation bias, or incorrect conclusions.

Challenge the analysis from the perspectives of:

- A skeptical subject-matter expert.

- A decision-maker responsible for financial outcomes.

- A technical reviewer checking the evidence.

- A project manager evaluating execution feasibility.

Identify the three most consequential weaknesses.

Correct what can be corrected using available evidence.

Clearly disclose what remains uncertain.

Present the revised recommendation rather than merely appending criticism.

Never invent evidence to resolve a disagreement.»

Prompt 5: The Daily Agency Brief

«Using my current project registry and any accessible recent updates, prepare a daily executive brief.

Include:

Reality Check: What actually matters today, including anything overdue, blocked, or at risk?

Today's Outcomes: No more than three meaningful outcomes worth pursuing.

Agency Move: One proactive action that advances a longer-term objective rather than simply responding to incoming work.

First Action: The exact next step I should take, specific enough to begin immediately.

Waiting / Do Not Touch: Projects that should not consume attention until a dependency changes.

Protect the highest-priority commitments from lower-value distractions.

Do not manufacture urgency to fill the schedule.»

The Architecture

The important part is how these prompts work together:

Conversation → Analysis → Adversarial Review → Decision → Project State Update → Next Action → Follow-up

The GitHub registry serves as durable state. Individual conversations are working sessions, not the ultimate source of truth.

This isn't fully autonomous. It still depends on tool availability, correct retrieval, explicit permissions, and disciplined state updates. The prompts are governance instructions, not guarantees of execution.

But it's a much more useful approach than maintaining dozens of disconnected chats.

What I'm Trying to Solve Next

My biggest remaining challenge is reliable continuity across conversations.

An AI can produce an excellent analysis today and still fail tomorrow if it doesn't retrieve the correct project state or recognize that a dependency has changed.

I'm interested in how others are approaching this.

Has anyone built a similar system using ChatGPT, Claude, GitHub, MCP tools, or another external source of truth?

And more importantly: how do you verify that your AI assistant actually completed its actions rather than just generating convincing status updates?

Tailoring an application properly is five separate prompts, and I used to paste them one by one, every single time. Now it's one chain. Here it is. Paste the job post and your resume where it says.

Step 1

Here is a job post: {{job_post}} List the requirements in order of how much they seem to matter to this employer. Mark which are must-haves and which are nice-to-haves, and quote the line each one comes from.

Step 2

Here is my resume: {{resume}} Go through each requirement from step 1 and say where my resume covers it, partly covers it, or doesn't. Be blunt about the gaps. Don't stretch anything to make it fit.

Step 3

Rewrite my five most relevant resume bullets so they line up with the must-haves. Use only experience that's actually in my resume. If a bullet can't honestly cover a requirement, leave it alone and tell me.

Step 4

Write a cover letter under 150 words. First sentence names the role and the strongest match. Address the biggest gap once, honestly, with what I'd do about it. No "I am excited to apply".

Step 5

Give me the five interview questions I'm most likely to get, based on the gaps you found, and one sentence on how to answer each.

The order matters. Step 2 makes it find the gaps before it writes anything, so steps 3 and 4 can't paper over them.

I run it with AI Toolbox, the extension I build. Type .. in the message box, pick the chain, and it asks for the job post and resume once in a single form, then runs all five steps in order. It works the same on ChatGPT, Claude, Gemini and Grok. Two saved chains are free. Sharing one library across all four assistants is part of Premium.

Guys how do you generate actually realistic pics without all the fake AI giveaways? I basically use my own face as a reference but it still doesn't come out that good. The face changes a bit, skin looks too smooth, hair looks fake, lighting/background looks AI. I have the ChatGPT Go subscription btw. How do you guys get your pics to actually look like real photos?

I work at a digital marketing agency, so a big part of my week is writing website proposals and pitch decks. For a long time I used ChatGPT to polish the wording, and the proposals came out sounding nice but generic.

Now, before anything goes to a client, I paste it in with this prompt:

>"You're the client receiving this proposal. You're busy, a bit skeptical, and you've seen 5 other agency pitches this week. Give me the 5 reasons you'd reject this or ghost us, ranked by how likely they are. Don't be polite."

Some things it caught that I missed:

• Example 1: Hospitality client's proposal listed features but never tied them to bookings or revenue. Its feedback was "I don't care about a CMS, I care about more reservations." It was right.
• Example 2: Pricing section showed one big number with no breakdown, which reads as "overpriced" at first glance. I split it into phases and the client signed the first phase.
• Example 3: A pitch deck spent 6 slides on us and 2 on the client's actual problem. I flipped the ratio.

What I've learned after doing this for a while:

1. Give it a persona with pressure. "Busy, skeptical, seen 5 pitches" gets much sharper feedback than "review this."
2. Ranking by likelihood stops it from listing 20 edge cases nobody cares about.
3. Follow up with "Now tell me the one thing that would make you say yes today." That answer usually becomes my opening line.
4. It's not always right. Sometimes it invents objections a real client would never raise. Use it as a stress test, not the final word.

It's like having a tough client review every proposal before the real one sees it.

Anyone else use ChatGPT for stress-testing their work instead of writing it? I'd love to know what personas or prompts work for you, especially in sales, freelancing or agency work.

This is something I do for fun, I like to take image prompts and asking AI to improve the prompt and explain its suggested changes before generating anything.

The problem I’m running into is when I upload pictures of my son that has Down Syndrome EVERYTHING I try does not come back to resemble my son at all, I pretty much just gave up. I’d love to run some cool family prompts that show us in different settings. I tell it to use the uploaded images to portray my son exact features without altering them at all. Once I told it my teen son has DS along with other guidance and it returned a completely different person with DS features. I realized that was the wrong description. My son does not have a defined jaw line and it’s just not working. For all other family members I can get to look just like us. Appreciate any feedback on this.

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One change I've been experimenting with when using AI for programming is separating the planning stage from the coding stage.

Instead of:

"Build this application for me."

I prefer something like:

"Analyze this project requirement. Break it into components, identify potential edge cases, and propose an implementation plan. Don't write the code yet."

Then I review the plan before asking for the implementation.

This makes it easier to catch misunderstandings before they turn into hundreds of lines of generated code.

I also like asking:

"Before giving me the final code, list the assumptions you're making."

AI-generated code can be useful, but blindly accepting it can create problems that are harder to find later.

How do you use AI when programming?

Hey guys, I’ve been juggling so many prompts lately, and honestly, it’s getting kinda hard to keep track of the ones I actually need or find an old prompt in my history 😅

How do you guys manage your prompts? Any recommendations for organizing or saving them?

Thank you

I have this vague idea to take a picture of a shelf and ask AI “Are there any good deals or mismatched items here?” or “Can you compare the prices of X item here to brick and mortar, as well as online retailers, to find the best deal?”

I’d like a cheat code to find steals. Sort of like those mistakes penny deals you see on fake videos.

Folks,

I have tried writing complete blog post using chatgpt. I was not sure which one would be best and tried to write the entire blog at once. But again what I have seen is if I go section by section, my first draft and the output is very decent and close to what I need.

I am still not sure if that is happening with you all? May I know what is working for you guys?

Small thing that took me way too long to notice: words like "slightly left," "off-center," or "toward the edge" in a composition instruction are basically decorative. The model interprets them however it wants, and the actual result drifts all over the place between generations — sometimes dead center, sometimes way off, with zero consistency. What actually fixed it: replacing vague positioning language with an approximate numeric anchor. Instead of "slightly left of center," something like "position the visual center of the subject at approximately 35–40% of the frame width from the left edge." Same idea for "near the top," "lower third," etc. — give it a number, not a direction word. It's a tiny change but it's the difference between a composition you can actually reuse across a batch of images and one that's a coin flip every time. Anyone else have a "small wording change, big consistency improvement" moment like this? Curious what other vague terms turned out to be silently useless.

Paste in one LinkedIn post you like and ask for five more "in this style." All five open with the same kind of hook, run the same three-paragraph shape, and end on the same kind of line. Give it a sample email that starts with "Hope you're doing well" and every email it writes starts that way. Show it an 80-word product description and every new one comes out at about 80 words.

You meant "this tone." It heard "this length, this structure, these openers, this tone." With one example, it can't tell which parts you liked and which parts were just how that one happened to come out, so it keeps all of them.

What fixes it:

1. Give it more than one, and make them different on purpose

Here are 3 examples of what I want: [examples]. Before writing anything, tell me in one line what they all have in common, and in one line what's different between them. Keep what's common. Vary what's different.

Pick examples with different lengths and openings. Whatever they share, it treats as the rule. Whatever differs, it treats as free.

1. When you only have one, say what to take from it

Here's an example of the tone I want: [example]. Copy the tone only. Don't reuse its length, its structure, its opening, or any of its phrases.

1. Check what it borrowed

Compare what you wrote to my example. List every phrase, structure, or length choice you took from it.

The third one is the eye-opener. It usually finds the shared opener and the matching length, and it often finds a phrase lifted word for word that you skimmed past.

Number 2 is saved in AI Toolbox for me, the extension I build, with the example as a {{variable}}, so it asks me to paste the example and drops it in the right spot.

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Many of us use ChatGPT in a very simple way, but the way we structure our prompts can completely change the results.

A good prompt structure usually includes:

• Role/Context: Tell ChatGPT who it should act as (e.g., "Act as a senior Python developer").

• Clear Task: Define exactly what you want it to do.

• Constraints/Format: Specify limitations or the desired format (e.g., "Summarize in bullet points" or "Avoid complex technical terms").

What prompt techniques do you use most in your daily life?

Let's exchange ideas in the comments!

So I give chatgpt image or of my model and the images kf my product that is dress/coat/sweater/tops etc it generates it. But the quality lacks the image and the poses of the model doesn't satisfy me what can i do to improve it

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One thing I've learned from using AI assistants is that the first answer doesn't always need to be the final answer.

Instead of immediately asking for a completely new response, I often ask ChatGPT to critique its own answer.

For example:

"Review your previous answer. Identify anything that is unclear, unsupported, unnecessarily complicated, or potentially incorrect. Then provide an improved version."

Another useful variation is:

"What assumptions did you make when answering this question?"

This can reveal missing context that I didn't realize I had left out.

Obviously, this doesn't guarantee that the answer is correct. The output still needs to be checked when accuracy matters.

Do you usually refine your prompts after the first response, or start over with a new prompt?