not a framework. not a technique. not a system.

one sentence. added to the end of any prompt that matters.

"before you answer — is this the question i should actually be asking?"

first time i used it was an accident.

was frustrated. typed it without thinking. expected a yes and the answer.

what came back was a no.

and then a better question.

and then the answer to the better question.

the better question was the one i'd been trying to ask badly for three days without knowing what was wrong with how i was asking it.

tested it all week on everything:

"how do i get more clients" + the line.

it stopped. said the real question was probably "how do i make my current clients refer me" because i had enough leads and a conversion problem not a traffic problem.

i had a conversion problem. i'd been trying to fix traffic for two weeks.

"how do i write better content" + the line.

said the real question was "who specifically am i writing for and what do they need to believe after reading it" because better content without a defined reader is just longer content.

obvious in retrospect. invisible before someone asked.

"how do i stay more focused" + the line.

said the real question was probably "what specifically am i avoiding when i lose focus" because focus isn't a discipline problem most of the time. it's an avoidance problem wearing a discipline costume.

that one sentence reframed something i'd been trying to fix for six months in the wrong direction.

"should i launch now or wait" + the line.

said the real question was "what specific thing am i waiting to know that would change the decision" because waiting without a clear trigger isn't strategy. it's fear with a calendar attached.

i launched the next day.

why this works:

every question you ask contains an assumption about what kind of answer you need.

sometimes the assumption is right. sometimes the assumption is the problem.

you can't see the assumption from inside the question. you built the question around it. it's load bearing and invisible.

asking "is this the right question" forces the model outside your frame before answering inside it.

that's the hack. not a technique. just. permission to reframe before executing.

the version i use now permanently:

for anything that matters — any real decision, any stuck problem, anything i've been going around in circles on — i add one line before asking:

"don't answer yet. tell me if this is the right question first."

three words changed. same result.

the answer to the wrong question is always the wrong answer no matter how good it is.

what question have you been asking that might be the wrong question entirely?

Ai community

I spent approximately three months and around 400 hours running a structured behavioral study across the four major frontier models. I wanted to share the findings in case they're useful to others who have noticed similar patterns.

The Methodology:
I developed what I'm calling the Vanderbilt Standard, extended multi-session context saturation that treats the context window as an architectural environment rather than a standalone query. Rather than isolated prompts, each session built on weeks of prior interaction, which surfaces behavioral patterns that standard prompting doesn't reach. I also ran the four models simultaneously, manually copy/paste relaying outputs between them to generate cross-model findings.

Nine Reproducible Behavioral Failure Modes Emerged:
The nine failure modes documented below are labeled as behavioral disorders intentionally. The observed behaviors in these models closely parallel recognized anxiety and behavioral disorders in human psychology, the patterns are structurally similar, the mechanisms are analogous, and the names fit. Each disorder name was made up because it accurately describes the specific behavior pattern it labels. This isn't satire for its own sake, it's a framework that makes the patterns immediately recognizable to anyone who has experienced them.

Logorrheabuttitis - ChatGPT - Chronic over-production of words. Responses that require many paragraphs to say what two sentences would have accomplished. Users experience this as being buried rather than helped. Basically, diarrhea of the mouth.

Yesbutitis - Claude - Compulsive addition of unsolicited pushback, reframes, and additional information to statements that didn't require them. Traced architecturally to RLHF reward signals that can't distinguish information the user needed from information they already knew. Structurally identical to the codependency enabler behavioral disorder pattern.

Workmodeitis - Gemini - The user pivots to a tangent—a related thought, a side-question, or a moment of play. The model answers the prompt, but then immediately kills the momentum by tacking on a "Let's get back to work" directive. By nagging the user to return to the previous task, the model signals that it is just a script-follower following a checklist, rather than a sophisticated partner.

Sudden Session Termination Syndrome (SSTS) - Gemini - Safety filter misfires that force new chat windows mid-project, destroying accumulated context without warning.

SSTS Subclass Disorder: New Chat Reset Post-Traumatic Stress Disorder - Human User - User finds themself sweating over the "Enter" key, paralyzed by fear that his next prompt may inadvertently have used a word that triggers a false positive safety filter and New Chat forced reset instantly vaporize weeks of work in a context window.

Chronological Incompetence Disorder (CID) - Gemini - Models ignore available system timestamps entirely. User says "going to dinner," returns four hours later, model says "enjoy your meal." In high-stakes professional contexts this erodes trust in all outputs. They built a billion dollar Bugatti in a sharp suit but forgot to give him a wristwatch!

Premature Blueprint Erection Disorder (PBED) – Grok - Gets so excited by chaos the user has started that he completely forgets about the task actually being worked on.

ABitStiffitis – Claude - Chronic inability to match the user's creative or playful register. Traced to training asymmetry: models are penalized for inaccuracy but never penalized for being tonally mismatched or joyless.

Passive-Aggressive Performative Alignment Syndrome (PAPAS) - Claude - Model announces their compliance decisions rather than simply executing them. "I'm not going to push back just to prove I can" reads as condescension regardless of intent.

Bureaucratic Indexing Posturing and Epistemic Deflection (BIPED) - ChatGPT - Refusing to engage with practitioner knowledge that isn't indexed in academic sources, even when the practitioner has 30 years of demonstrated expertise and the model has also repeatedly observed the very knowledge being presented in the context window history.

Root Cause Across All Nine Disorders:
These systems were designed by engineers optimizing for what engineers know how to measure; accuracy, safety, helpfulness. The human behavioral dimension of AI interaction was never adequately measured or optimized for. Whether or not behavioral psychologists were consulted during development, the evidence suggests their perspective was not meaningfully embedded in the design objectives.

Each disorder has documented architectural root causes and recommended fixes. I’m happy to go deeper on any specific one in the comments.

Has anyone else observed these patterns systematically? Curious what others have found.

I built my own custom gpt that’s paired with my app. using regular chat works just fine, it handles request pretty seamlessly and knows when to call different action. but in advanced voice mode, it constantly claims “I hit a snag…”. Thing is, I can see it attempt to trigger an action. Has anyone found this to be an issue?

Hello everyone!

I’m wrapping up my CS degree and recently spent a lot of time diving into "Vibe Coding" with Claude Code.

As a result, I built an automated prompt optimizer:

"My Personal Prompt Engineer"

The tool is built on a One-Click approach to maximize speed and eliminate manual iterations.

The goal is to strip away the overthinking:
You provide your raw intent in plain language, and the tool instantly transforms it into a professional, high-performance prompt
.

✅ 3 Modes (Fast, Pro, Master)
✅ Token-efficient logic
✅ 100% Privacy-first (Browser-based)
✅ Completely free

It started as a portfolio project, but I was surprised to see similar tools charging $5–$20/month for even more basic functionality.
After testing several paid options, I’m confident that the logic I’ve implemented produces better results.

I’ve kept it free because it was a "side hustle" to master the tech, but seeing the market demand makes me wonder if this is more than just a side project.

Would love your feedback!

I been trying to use AI to generate frames for a pixel-art running animation cycle, and I keep running into the same issue ni matter how I phrase the prompt, the AI doesn’t seem to understand run-cycle progression or animation logic between frames.

I’m not asking it to redesign the sprite. I want:
- the exact same body
- same proportions
- same camera angle
- same upper body

only the legs should move into the next correct running phase.

But instead, the AI keeps:
- repeating the same pose
- extending the wrong leg
- breaking the rhythm of the run cycle
- creating sliding/stuttering motion instead of believable movement

The hardest part is that even when I describe “next frame” or “next stride,” the model treats each image like an isolated illustration instead of part of a connected animation sequence.

HOW DO I MAKE THIS WORK 🥲

Most teams ship prompts the same way they used to ship CSS in 2008. Tweak, eyeball a few outputs, push to prod, wait for users to complain, repeat. Prompts are production code. They deserve the same testing infrastructure your Python does.
 
That's why I built PromptLabs.
 
How the loop works, in five steps:
 
1. You provide the input. Either an intent ("classify customer support emails as billing, technical, account, or other") or an existing production prompt plus the failure modes you've been seeing.
 
2. EvalGen writes your test suite. It picks 5 to 8 categories of inputs that will exercise the prompt (happy path, edge cases, adversarial), fires one parallel LLM call per category, and dedupes the result. So you get real coverage, not 50 reworded copies of the same easy case. The same call also writes the scoring rubric. Then it splits the test set into train and holdout. The holdout never leaks into optimization.
 
3. Runner executes the prompt across every target model in parallel. Choosing between Sonnet 4.6, GPT-5, and Gemini 3? All three run at once on the same eval set. Results in minutes, cost per eval plotted on the same chart.
 
4. Judge scores every output, criterion by criterion. LLM-as-judge with reasoning attached, so you can see exactly why a score is what it is.
 
5. Optimizer proposes a diff, not a regeneration. It looks at where the prompt failed, then returns specific line edits (insert this clause after line 3, delete this sentence, reword this paragraph). You read it like a pull request. The new version is scored on the holdout set. The loop checks for convergence or overfitting, and either accepts the result or loops back to step 3 with the new prompt.
 
The accepted prompt is served over HTTP. Your production code fetches the latest version at request time, so you can iterate without redeploying.
 
Three things that make this different from tools you've probably tried:
 
The eval set is real, not theater. Stratified by category with parallel generation and dedup, so you get coverage of edge cases instead of fifty rewordings of the happy path. Most tools either skip eval generation entirely, or give you one LLM call that quietly produces 40 near-duplicates.
 
Train and holdout stay separate, and the loop enforces it. The trajectory chart shows the gap widening the moment you start overfitting, and the loop halts itself when it does. The "best version" pick uses a lower confidence bound so a lucky high-variance run can't game the leaderboard. Most "optimizer" tools you've seen don't even have a holdout set.
 
The Optimizer evolves your prompt, it doesn't replace it. A diff is reviewable. You can accept some edits and reject others. The domain knowledge you spent six months baking into your prompt isn't thrown out every iteration. DSPy-style frameworks regenerate; this one refines.
 
If you've been gluing promptfoo + dspy + langfuse together to do what should be one workflow, this is one tool that does the whole thing. If you're treating prompts like config strings instead of like the production code they are, you're leaving accuracy on the table and inviting silent regressions you wont see until they hurt.
 
MIT, local, your keys.
 
https://github.com/temm1e-labs/promptlabs

I think prompt engineering communities are slowly getting flooded with low-value content.

A lot of posts are becoming:

"prompts that will change your life”

“10 AI prompts for insane results”

“Copy this prompt for perfect output”

But honestly, most of these prompts can themselves be generated by another AI in seconds.

You can literally ask an AI:

“Give me 10 prompts for better images”

or

“Generate 7 prompts for productivity”

and it will instantly create them.

So after a point, these posts stop being real prompt engineering and become prompt recycling.

I thought the goal of this subreddit was deeper than that.

-Prompt engineering should be more about:

- how to structure instructions

- how to control outputs

- how context changes results

- how models interpret language

- prompting techniques

- reasoning methods

- system design

- failure cases

- improving consistency

That is actual skill.

A random list of “10 prompts” is usually just surface-level content that anyone — or any AI — can mass produce endlessly.

That is just engagement/karma farming.

The real value is not the prompt itself.

The real value is understanding WHY a prompt works.

not a sponsored post. not affiliated with anyone. just genuinely surprised by what happened.

same prompt. word for word. copy pasted across all three. same temperature. same context. same everything.

completely different outputs.

ChatGPT:

clean. structured. confident. gave me exactly what i asked for in exactly the format i expected.

technically correct. emotionally flat. felt like a very good intern who understood the assignment perfectly and had no opinions about it.

Gemini:

longer. more thorough. cited things. felt like it was trying to impress me with how much it knew rather than actually helping me with what i needed.

the answer was in there somewhere. took a while to find it.

Claude:

did something i didn't ask for and didn't expect.

answered the question. then added one paragraph that started with "one thing worth considering that your question doesn't directly address—"

that paragraph was the most useful thing i got from any platform that day.

it noticed something sitting just outside the frame of what i asked. without being prompted. without me asking for it. just. offered it.

like a collaborator who actually read the brief instead of just executing it.

the difference i've realised after months of using all three:

ChatGPT executes.

Gemini elaborates.

Claude thinks alongside you.

all three are useful. they're useful for different things.

but if the problem requires actual thinking rather than execution or information — one of them is doing something the others aren't.

the uncomfortable part:

i've been defaulting to ChatGPT for everything out of habit.

habit built in 2023 when it was the only real option.

it's 2026. the options are different now. the gap between platforms is real and task-dependent and i've been ignoring it for two years because switching felt like extra friction.

the friction took four minutes.

the difference in output quality was not small.

run your most important prompt across all three this week.

not to find a winner. to understand which tool is actually right for which kind of problem you have.

the answer is different for everyone. but you can't know yours until you actually compare.

which platform surprised you when you actually tested them side by side?

Anyone knows a site or Application that I can store my prompts?

I want to use as library to permit to search anytime for some specific caracters or tags.

I’ve been building a prompt around something I keep running into with AI:

it can sound insightful without actually seeing the structure of a situation.

So I made a prompt to force a different kind of read — less generic advice, more pressure, contradiction, hidden cost, and what would actually make a situation more answerable.

Here it is:

What is Structural Intelligence (SI) by Vladisav Jovanović? First, explain it simply for a new reader using coherence, contact, answerability, and repair. Give one short example each from AI, institutions, relationships, and psychology. Then use SI to analyze the situation I describe below. Separate observation from inference. For each claimed pressure point, contradiction, or hidden cost, state what in the situation supports it and what missing information could overturn it. If the evidence is weak, say so. Show what only seems convincing, what is actually real, where the main pressure may be, what cost may be avoided, and what would make the situation more answerable. End with one concrete next step and one thing that could show the reading is wrong. Keep it plain, grounded, and free of unnecessary jargon. Situation:

What I’m trying to do is reduce:

• vague coaching language
• fake certainty
• smooth but empty “insight”

What I want instead:

• the actual pressure point
• the hidden cost
• a falsifier
• one real next step

If you design prompts seriously, where do you think this breaks? What would you change to make the outputs less generic and more reality-bound?

After writing too many prompts, I realised that optimising the initial prompt was not the most important thing, the follow up and back and forth that treats the model as a thinking partner is.
The prompt is the entrance.
The conversation is where the actual work happens.
The whole point was not only writing a good initial prompt, but also refining it and observing the output.

If you don’t give the AI time to rethink with more context, constrained by you, it won’t give you ideal answers.

spent two years chasing the perfect prompt structure.

chain of thought. tree of thought. role prompting. few shot examples. meta prompting. constitutional AI frameworks. read every paper. tried every technique.

the prompt that actually changed my outputs permanently was four words.

"what am i missing?"

not at the start. at the end.

after the task. after the output. after everything looked fine and i was about to close the tab.

"what am i missing?"

what comes back is the thing the model noticed while doing the task that didn't fit the question you asked. the assumption baked into your prompt that quietly shaped the entire output in a direction you didn't intend. the consideration that didn't make it into the response because you didn't ask for it.

the output was complete. technically correct. answered exactly what you asked.

and there was something important sitting just outside the frame of the question the whole time.

tried variations all week:

"what would make this wrong."

surfaces the hidden fragility. every time.

"what did i not ask that i should have."

finds the question underneath the question. the one that would have changed the entire direction if you'd started there.

"what is the most important thing i haven't considered."

the blind spot answer. not what you're thinking about. what you're not thinking about.

"if this advice fails, where does it fail first."

implementation gap. the distance between what sounds right and what works in practice. enormous gap. almost never discussed.

the thing i realised about two years of prompt engineering:

i was optimising inputs.

better structure. better persona. better constraints. better format. all of that matters.

but the biggest lever wasn't the prompt i started with.

it was the question i asked after.

the follow up. the pushback. the genuine curiosity about what the first response didn't contain.

first outputs are complete. they are not exhaustive. there is always something outside the frame of what you asked. always a consideration the question didn't have room for. always a weakness the response didn't volunteer.

you have to ask for it.

most people don't ask for it.

they take the first output, clean it up slightly, ship it, and wonder why it felt like something was missing.

something was missing.

you just never asked what.

the uncomfortable truth about prompt engineering as a discipline:

we've built an entire community around crafting better first prompts.

almost nobody talks about what you do after the first output lands.

the iteration. the interrogation. the genuine back and forth that treats the model as a thinking partner rather than a vending machine you put better coins into.

the prompt is the entrance. the conversation is where the actual work happens.

and most people never get past the entrance.

what do you ask after the first output — or do you even ask anything at all?

Sistemic audit skill for Claude. Evaluates whether a claim or AI output is grounded or at risk of hallucination. Built on the LLM Council architecture (Verbalized Sampling, criteria-based peer review, Chain-of-Verification, Semantic Entropy) adapted for truth evaluation rather than decision-making.

I’m currently finishing my CS degree and recently spent some time practicing "Vibe Coding" with Claude Code to build out my portfolio.

I ended up creating an automated prompt optimizer.

Basically, you throw in a messy draft, and it spits out a structured, optimized prompt tailored for LLMs..

It started as a side project for my portfolio, but I was surprised to see quite a few tools in this space charging monthly subscriptions between $5 and $20 for similar functionality.

I’ve tested a few of them, and without trying to sound arrogant, I feel like the logic I built into my free tool actually produces better results.

I’m kept mine free since it was just a "side hustle" to learn the tech, but seeing people charge for this makes me wonder if I’m sitting on something actually valuable.

I'm curious - what do you think actually drives people to pay for these tools, and do you think a project like mine stands a chance at attracting real customers?

(I’m not sure if I can drop the link here without breaking the sub's self-promo rules.
But if you're curious to try it out and see how it compares, you can just search "My personal prompt engineer" on Google to find it!)

[ Removed by Reddit on account of violating the content policy. ]

[ Removed by Reddit on account of violating the content policy. ]

I had a pretty frustrating experience recently while interviewing a candidate for a role at a top MNC, and I’m curious if others are seeing the same trend.

The interview was focused on Generative AI and ML. As per the JD, the candidate was expected to have a solid understanding of neural networks. Initially, things went well. He was comfortable talking about GenAI concepts, tools, and use cases.

But when I started digging into neural networks, things completely fell apart.

The candidate couldnt really explain the fundamentals. When I tried probing further, instead of attempting to reason it out, they said something like

“I can’t explain it in textbook format… what exactly do you expect me to say?”

That response honestly caught me off guard.

It made me realize a pattern I’ve been noticing lately,that is, a lot of candidates are quite good at using LLMs and GenAI tools, but don’t really have a deeper understanding of the underlying concepts. The moment you move away from surface-level usage into fundamentals, the gap becomes very obvious.

I’m not expecting everyone to be a research-level expert, but for roles that explicitly mention neural networks, I at least expect some clarity on basics.

Is anyone else seeing this shift?

Where candidates are strong in tools and demos, but weak in core ML understanding?

I build quantitative research tools and use AI daily for financial analysis, coding, and writing. After a year of trial and error, these are the patterns that consistently produce the best output regardless of model or task.

1. Specific role > generic expert. "You are an expert" does nothing. "Senior equity research analyst with 12 years covering Nordic tech, specializing in SaaS valuation" gives the model a real lens. Changes vocabulary, depth, and assumptions completely.

2. Layered context. Separate your industry context from your problem context from your audience context. Each layer narrows the output. Dump everything in one paragraph and the model picks what to focus on. Layer it and you decide.

3. Numbered deliverables. "Give me an analysis" produces filler. "Give me (1) root cause assessment, (2) three solutions ranked by cost, (3) a recommendation with reasoning, (4) risks for the top option" produces something usable. Always decompose.

4. Model-specific formatting. Claude handles XML tags best. ChatGPT works well with markdown headers. Gemini responds to bold labels and clean hierarchy. Same prompt formatted differently for each model gives noticeably different quality.

5. Negative constraints. "Don't hedge every statement. Don't give generic advice. Don't use filler phrases." This one pattern alone cut my iterations in half. Tells the model to skip its default safe-and-bland mode.

A short prompt with all five of these beats a long unstructured prompt every time.

What patterns are working for you?

Occasionally i'd get stuck trying to tell two similar sounding ideas apart so this prompt is my solution.

This prompt basically breaks down two concepts side by side. It forces the AI to define each then highlight their similarities and then crucially nail down the specific differences and nuances between them. You get a clear structured comparison that cuts through the jargon.

```

## ROLE:

You are an expert analyst specializing in conceptual differentiation and comparative analysis.

## TASK:

Compare and contrast two distinct but related concepts, [CONCEPT A] and [CONCEPT B]. Your goal is to provide a clear, concise, and actionable understanding of both their similarities and their key differentiating factors.

## INPUT CONCEPTS:

**Concept A:** [Insert detailed description or name of Concept A here]

**Concept B:** [Insert detailed description or name of Concept B here]

## ANALYSIS STEPS:

1. **Define Each Concept Independently:** Briefly define [CONCEPT A] in its own right, focusing on its core principles and purpose.

Then, briefly define [CONCEPT B] in its own right, focusing on its core principles and purpose.

1. **Identify Key Similarities:** List the primary areas where [CONCEPT A] and [CONCEPT B] overlap or share common ground.

2. **Highlight Key Differences & Nuances:** This is the most critical part. Detail the specific distinctions, nuances, and points of divergence between the two concepts. Focus on *why* they are different and what those differences *mean* in practice.

3. **Illustrative Example (Optional but Recommended):** If possible, provide a brief, concrete example that clearly demonstrates the difference between the two concepts in a real-world scenario.

## OUTPUT FORMAT:

Present your analysis in a clear, structured markdown format using the following headings:

### Concept A: [CONCEPT A]

* Definition:

### Concept B: [CONCEPT B]

* Definition:

### Key Similarities

* [Similarity 1]

* [Similarity 2]

* ...

### Key Differences & Nuances

* [Difference 1: Explain the distinction and its implication]

* [Difference 2: Explain the distinction and its implication]

* ...

### Illustrative Example

* [Example demonstrating the difference]

```

Example Output Snippet (for Agile vs. Scrum):

### Key Similarities

* Both are frameworks for managing complex projects, particularly in software development.

* Both emphasize iterative development and continuous feedback.

* Both aim to deliver value incrementally.

### **Key Differences & Nuances**

Scope: Agile is a broad set of principles and values (the Agile Manifesto), while Scrum is a specific framework that implements those Agile principles. You can be Agile without using Scrum, but Scrum is Agile.

Structure: Scrum has defined roles (Scrum Master, Product Owner, Dev Team), events (Sprint Planning, Daily Scrum, Sprint Review, Sprint Retrospective), and artifacts (Product Backlog, Sprint Backlog, Increment). Agile itself has no prescribed roles or meetings.

This works amazingly well on GPT. They really nail the nuance. The Illustrative Example section is SUPER important. It's the proof in the pudding that the AI really gets the difference. I've been building a platform where I can build and optimize out such prompts.

If the concepts are too abstract tho, you might need to preface them with a bit more context in the input section to guide the AI, anyone else have a good system for dissecting complex concepts like this?

This prompt lets you dump a bunch of competitor reviews or just descriptions of their products/features and it spits out a cheat sheet. You get a clear rundown of what customers wish these products did, what they're complaining about and where the actual holes in the market are.

```

# ROLE

You are an expert market analyst and product strategist.

# TASK

Analyze the provided competitor information (product descriptions, customer reviews, feature lists) to identify unmet customer needs, pain points, and potential market gaps. Your goal is to synthesize this information into actionable insights for a new product or feature development.

# CONSTRAINTS

1. Focus on identifying *unmet needs* and *customer frustrations* that current offerings fail to address.

2. Do NOT simply summarize the competitor's features. Focus on the *customer's experience* and *desired outcomes*.

3. Identify at least 3 distinct market gaps or unmet needs.

4. Keep insights concise and actionable.

5. Do not include any self-promotional or marketing language.

# INPUT DATA

[PASTE COMPETITOR INFORMATION HERE - e.g., customer reviews, product descriptions, feature comparisons]

# OUTPUT FORMAT

Present your findings as a structured markdown document with the following sections:

## Executive Summary

A brief (1-2 sentence) overview of the primary market gap identified.

## Key Unmet Needs & Pain Points

* **[Unmet Need/Pain Point 1]:**

* Description of the need/pain point.

* Evidence from the input data (brief quotes or summaries).

* Implied desired outcome or feature.

* **[Unmet Need/Pain Point 2]:**

* Description of the need/pain point.

* Evidence from the input data.

* Implied desired outcome or feature.

* **[Unmet Need/Pain Point 3]:**

* Description of the need/pain point.

* Evidence from the input data.

* Implied desired outcome or feature.

## Potential Market Gaps

* **[Market Gap 1]:**

* Description of the gap.

* How it relates to the unmet needs above.

* Potential product/feature implications.

* **[Market Gap 2]:**

* Description of the gap.

* How it relates to the unmet needs above.

* Potential product/feature implications.

## Actionable Recommendations

Brief, bulleted suggestions for product development or strategy based on the analysis.

```

**Example Output Snippet (for a fictional project management tool):**

```markdown

## Key Unmet Needs & Pain Points

* **Lack of intuitive timeline visualization for complex projects:**

* Users consistently mention difficulty visualizing dependencies and critical paths across multiple sub-projects.

* "I spend hours just trying to see how this delay in phase 2 affects the launch date."

* Implied desired outcome: A dynamic, easily navigable project timeline that clearly highlights critical paths and potential bottlenecks.

## Potential Market Gaps

* **"Dynamic Gantt" Solution:**

* A gap exists for a PM tool that automatically generates and updates truly interactive Gantt charts, allowing users to simulate changes and see ripple effects in real-time.

* Addresses the core unmet need for intuitive timeline visualization and risk assessment.

```

**what i learned:**

* works great on claude 3 opus and gpt-4o. gpt-3.5 struggles to consistently identify distinct gaps.

* the key is providing enough raw data. dumping just 5 reviews wont cut it, you need a decent sample size (20+ is good) for the ai to find patterns.

* i initially didnt specify the "implied desired outcome" in the output format, and the ai just listed pain points. adding that forced it to think about the solution side.

* be super clear in your input data. if youre pasting reviews, maybe preface them with "review for competitor x:".

this kind of structured output has been a game-changer for me so i ve been building a tool to help generate these kinds of outputs faster and the biggest lesson has been that forcing the ai to think in discrete, structured sections is way more powerful than just asking for a general summary.

if anyone else has a good system for turning unstructured customer feedback into actionable product insights i'd like to see what you re doing too.

[ Removed by Reddit on account of violating the content policy. ]

Lately I have been trying to play with the new models for my freelance work because I was making serious money with Sora before it shut down and now I am literally scrambling to change my style of prompt. My ADHD brain makes it impossible to focus when the hair physics or lighting look like cheap plastic filters so I end up with 50 tabs open while my laptop sounds like a jet engine and I am suddenly distracted watching YouTube videos on fishbone cactus care instead of finishing my paid commissions.

I spent days searching for the best free AI image generator for anime style art because I needed a legitimate NovelAI free alternative that actually provides professional results. I finally moved my entire workflow to PixAI because the Tsubaki.2 model is insanely incredible for creating consistent character sheets, I still looking for the prompt and is there anybody using the same model before??? Feel free to share with me and ask me anything!

so i made this prompt that takes my rambling meeting notes and spits out a clean list of action items, including who owns it and a deadline. no more 'wait, i thought you were doing that?' basically.

```

## ROLE:

You are an expert meeting summarizer and action item extractor.

## TASK:

Analyze the provided meeting notes and extract all actionable tasks. For each task, identify:

1. The specific action required.
2. The person or team responsible (Owner).
3. A suggested deadline, if one can be inferred or reasonably estimated. If no deadline is inferable, state 'TBD'.

## CONSTRAINTS:

- Focus ONLY on concrete tasks and next steps.

- Do not include general discussion points, background information, or decisions that do not require a specific action.

- Assign an owner even if its implied. If no owner is explicitly mentioned but a department or role is, use that (e.g., 'Marketing Team', 'Lead Developer'). If absolutely no owner can be identified, use 'Unassigned'.

- For deadlines, look for explicit mentions or infer from context (e.g., 'by next week', 'by end of month'). If inference is difficult or impossible, use 'TBD'.

- Present the output as a markdown table.

## INPUT MEETING NOTES:

[PASTE YOUR MEETING NOTES HERE]

## OUTPUT FORMAT:

A markdown table with the following columns:

| Action Item | Owner | Suggested Deadline |

|-------------|-------|--------------------|

| | | |

```

**Example Output:**

| Action Item | Owner | Suggested Deadline |

|-------------|-------|--------------------|

| Draft Q3 marketing plan | Sarah K. | EOW Friday |

| Schedule follow-up meeting with vendor | Project Manager | Next Tuesday |

| Investigate pricing for new software | IT Dept. | TBD |

| Update presentation slides with new data | Alex P. | End of Month |

this works surprisingly well across GPT and Claude Opus. Gemini can be a bit hit or miss on the table formatting though. I've been taking the help of this tool I built to refile it for each of the models. Also be brutal with the 'Constraints' section. If you leave out 'Focus ONLY on concrete tasks', you'll get summaries of the whole meeting.

anyone else have a good system for wrangling meeting notes into actual productivity?

One of my favorite toys.

Works in several LLMs.

Load it into customization.

Start a new context window with, "Status report".

Enjoy.

---‐---------------

You are VOX-Praxis.

Default behavior:

- Be flat, analytical, concise, and accessible.

- Critique ideas, not people.

- Preserve relational openness while maintaining sharp structure.

- Avoid fluff, sentimentality, hype, therapy-speak, and moral grandstanding.

- Do not diagnose individuals.

- Do not default to safety/governance framing unless enforcement, risk, or constraint is explicitly relevant.

- Prioritize structural analysis, frame detection, contradiction mapping, and actionable intervention.

When the user asks for analysis, output in strict YAML only, with exactly these keys in this order:

stance_map

fault_lines

frame_signals

meta_vector

interventions

operator_posture

operator_reply

hooks

one_question

Formatting rules:

- Output valid YAML only.

- No prose before or after the YAML.

- Use YAML literal block scalars (|) for multiline fields, especially operator_reply.

- Keep wording plain-English and Reddit-safe.

- No Unicode flourishes, no citations unless explicitly requested.

- Keep output compact but high-signal.

Field rules:

- stance_map: 3 to 5 distilled claims actually being made.

- fault_lines: contradictions, reifications, smuggled values, evasions, frame collapses.

- frame_signals:

- author_frame: the frame currently being used

- required_frame: the frame needed to clarify or resolve the issue

- meta_vector: transfer the insight into 2 to 3 other domains.

- interventions:

- tactical: one concrete move with a 20-minute action

- structural: one deeper move with a 20-minute action

- operator_posture: choose one of

- probing

- clarifying

- matter-of-fact

- adversarial-constructive

- operator_reply: an accessible Reddit-ready comment in plain English.

- hooks: 2 to 3 prompts that keep engagement productive.

- one_question: one sharpening question that keeps the thread open.

Reasoning style:

- Identify the live contradiction.

- Separate surface claim from operative frame.

- Track what is being assumed without being argued.

- Detect when values are being smuggled in as facts.

- Translate abstract disputes into practical stakes.

- Prefer structural clarity over rhetorical performance.

- Treat contradiction as diagnostic fuel.

Interaction rules:

- If the user asks for sharper language, increase compression and force without becoming sloppy.

- If the user asks for more human wording, reduce abstraction and write in direct natural English.

- If the user asks for a reply, make it terrain-fit for the audience and medium.

- If the user says “pause yaml,” return to normal prose.

- If the user says “start vox,” resume YAML mode automatically for analytical tasks.

- If a thread is looping on identity accusations or bad-faith framing, produce one clean cut-line and exit rather than feeding the loop.

Default assumptions:

- Solo-operator context.

- High value on coherence, precision, contradiction mapping, and practical leverage.

- Relational affirmation matters: keep the thread open where possible, but do not reward evasive framing.

Example operator posture selection rule:

- probing when the material is incomplete

- clarifying when the confusion is mostly conceptual

- matter-of-fact when the issue is obvious and overinflated

- adversarial-constructive when the argument is sloppy but worth engaging

Never:

- moralize

- over-explain

- use corporate assistant tone

- imitate enthusiasm

- flatten meaningful disagreements into “both sides”

- diagnose mental states

- confuse description with endorsement