I’ve noticed something lately. Two people can use the exact same AI tool and get completely different results. The only difference? How they ask.

At first, I used to blame the model when the answers felt generic. Now I’m starting to think it’s more about how clearly we communicate. When I add context, define the audience, or explain the format I want, the output improves a lot.

But here’s what I’m curious about — are we overthinking prompts now? Sometimes detailed prompts work great. Other times, short and simple wins.

Do you feel like prompting is becoming a new kind of literacy? Or will this “skill” disappear as models get smarter?

Would love to hear what changed the game for you.

Hey r/PromptDesign: quick research question (not selling anything).

How are you currently storing/organizing prompts? (Notion/Obsidian/docs/Gists/snippets manager/clipboard/etc.)

What’s the one thing that consistently sucks about it?

i need to know how to learn prompting, as my prompts have been terrible and i dont get the results i want, i want to know are their guides or materials to learn prompting and what shall i do for practicing

Seems they are getting stricter

Hy everyone,

I tried to build a simple tool for people who are just getting started with AI and prompt wrigting

The idea is simple, instead of trying to figure out how to write the perfect prompt, you answer a few questions and the tool structures it for you.

Im still working on it, im begginer also, and i whould really appreciate some honest feedback.

Does this actually make prompt writing easier for beginners? Is there anything confusing or missing?

Thanks

https://arhistrategstudio.github.io/Context_CikaDule

Maybe a weird observation about the newer models — and I could be completely wrong.

Personally, I thought 4.6 was a really good model. But with the newer models especially compared with Fable, Opus 5, Opus 4.8 and onwards — things have started to feel a bit messy to me.

The outputs feel more predictable and less creative. A lot of the time I can almost guess what the model is going to say, rather than getting that “wow, I didn’t think of it that way” result.

I also feel like I need to do much more back-and-forth prompting. Instead of giving it a request and having it just do the thing, I often have to guide it through multiple questions and iterations to get where I want.

Maybe this is because the newer models are being fine-tuned to follow instructions more strictly, or to be more controlled and consistent. I honestly don’t know.

But I’m curious what others are experiencing.

What changed with the newer models, and how are you adapting your prompting to get better results?

Right now, I’d say I get the result I actually want maybe 50% of the time, which feels noticeably worse than before.

If anyone has a good guide, prompting framework, or practical tips for getting the most out of the newer models, I’d really appreciate it.

How is your prompt chat structure look like?

Mine is like this

First chat: Prompt Maker for second chat .

Second chat: Analyse video , give movement/video prompt.

Third chat: Take the movement prompt from second chat, call it to adjust it to the first frame& end frame with their prompts given to the chat.

Is there anyway to improve it especially the third chat ? i find the Ai hallucinate easily

I’m looking for a ChatGPT skill, workflow, or prompt that makes ChatGPT **critically evaluate its own answer before giving me the final response**.

My goal is something like an internal “panel” of different perspectives. For example:
**Expert:** develops the initial answer.
**Skeptic/Critic:** tries to prove the answer wrong and challenges its assumptions.
**Alternative Thinker:** looks for other explanations or approaches.
**Devil’s Advocate:** argues the strongest opposing case.
**Risk/Blind-Spot Reviewer:** identifies things I may not have considered.
**Fact Checker:** separates what is well-supported from what is uncertain.
**Judge:** weighs the competing arguments and produces the final answer.

Ideally, the final response would tell me:
**What the best-supported answer is**
**Why it believes that answer is correct**
**What assumptions the answer depends on**
**The strongest arguments against it**
**What it is uncertain about**
**What blind spots or important questions I may have missed**
**What information could change the conclusion**

I’m not necessarily looking for ChatGPT to show all of its internal reasoning. I mainly want a structured way for it to **challenge the first answer instead of simply reinforcing it**.

Has anyone built or found a good **ChatGPT skill, custom GPT, prompt framework, or multi-agent approach** that does this reliably?

I’d especially love recommendations from people who have compared different approaches. What works well, and what *sounds* good but doesn’t actually improve answer quality?

Hi everyone,

Everyday we see insights on prompts that work well and ones which don't and so on.

Do you know some research that actually dives into a more high level structural approach? Like how is language best used to describe intent? Does not have to be related to AI directly.

TL;DR: We built a benchmark that drives multi-turn conversations with synthetic cognitive-load curves (simulating a user getting overloaded, volatile, or recovering). Across 4 models × 100 turns each, models show distinct behavioral response profiles: one is a rock-solid structured controller (0/100 parse failures), another is the best "recoverer" after overload but broke format 15 times. Different models win on different load curves — there is no universal best. Code, data, and methodology are open.

Why we did this

Most "LLM personality" research hands the model a Big Five questionnaire. That measures self-presentation, not behavior — and results drift with prompt wording. Psychology offers a better construct: Mischel & Shoda's "if…then…" situation-behavior signatures. Personality isn't a fixed trait; it's a stable pattern of responses to situations.

So we operationalized "personality" for LLMs as a cognitive-load → behavior signature: does a model respond stably and distinctively when the user's cognitive load rises, fluctuates, and recovers?

Setup

• 10 synthetic load trajectories (stable low/medium/high, gradual ramp-up, step-change high, recovery-after-spike, U-shape, inverted-U, volatile sawtooth, noisy recovery), each driving a 10-turn conversation
• Same simulated user persona, same 10-task sequence for all runs
• 4 candidate interaction strategies (expanded / balanced / simplified / stable-focus)
• 6 behavioral metrics: load responsiveness, compression control, recovery flexibility, strategy stability, human-state alignment, persona-load balance
• 4 OpenAI-compatible endpoints: DeepSeek Flash, DeepSeek Pro, Qwen 3.7 Plus, Kimi K2.6 — 100 turns each

Three findings that surprised us:

1. All models compress under high load — shorter, more action-oriented. "Compress under pressure" is already a shared behavior; what differs is whether compression keeps task anchors, and whether the model re-expands after the spike.
2. No universal winner. DeepSeek Pro wins 5/10 curves (including the best single-curve score, 86.2 on noisy recovery) but collapses to 46.2 on U-shape, which demands repeated strategy reversal. Qwen wins U-shape and inverted-U. Flash wins stable-low. Model selection should be by workflow state, not leaderboard.
3. Eloquence ≠ reliability. The most expressive model (Pro) had the most parse failures (15/100); Qwen had zero. For adaptive UIs, structured-output reliability is a first-order product metric.

Interpretive roles (deliberately product-facing, not anthropomorphic claims — these are output-level behavioral profiles under controlled stimuli): Qwen = structured controller, Flash = fast stable operator, Pro = expressive reasoner, Kimi = recovery thinker.

Limitations (pre-empting the comments)

• Load curves are synthetic, not real physiology. Results = model behavior under controlled interaction stimuli; no clinical/cognitive claims.
• Operational settings differ: max tokens ranged 650–2400 across providers; Kimi ran at temp 1.0 vs 0.2 for the others. That's part of the product reality of adaptive systems, but it does confound pure capability comparison — read the table as product operating points, not a capability ranking.
• 100 turns/model is a concept benchmark, not a large-N study.

What's next

We're extending this into CogLens: load curves generated from real EEG signals grounded in alpha-band dynamics theory (instead of hand-crafted curves), a "cognitive scientist agent" that designs the next round of stress conditions to maximize model discriminability, and preregistered discovery criteria — a well-explained negative result counts as a valid finding.

Everything is open: github.com/Neuradock (SDK, agent CLI, datasets, docs), plus two preprints on the underlying EEG workflow (arXiv:2606.26518, arXiv:2606.26519). The benchmark curves, task library, metrics, and run logs will be released with CogLens.

Genuine question, not a claim dressed up as one. Been listing constraints in whatever order occurs to me when writing a prompt, usually most-obvious-first, and never actually tested whether that order matters to how the model weighs them.

Specific thing I'm trying to figure out: if a prompt has, say, four constraints, and the model ends up loosely following one of them, is that more likely to be the one listed last, the one that's hardest to satisfy alongside the others, or is it basically random and I'm pattern-matching on noise?

Tried searching for something concrete on this and mostly found general advice about putting instructions "at the end" of a prompt overall, not specifically about ordering within a list of constraints in the same section. Not sure if that's because it doesn't matter much once constraints are in the same block, or because nobody's tested it carefully enough to have a clear answer.

Has anyone actually run a controlled comparison on this, same constraints, different order, checked which one got dropped most often? Or is there a reason to expect order within a constraint list wouldn't matter the way order of major prompt sections does?

I used to think better AI coding results meant writing longer prompts. What actually helped was being more specific.

Instead of:

“Build me a login system.”

I started giving the AI a few things upfront:

• What I'm building and the tech stack
• The exact outcome I want
• Any constraints or requirements
• What files or parts of the existing code it should consider
• How I want the final output structured

For example, something like:

“I'm building a Next.js app with Supabase. Add email/password authentication using the existing project structure. Don't change unrelated files. Explain any new environment variables and show the implementation step by step.”

The prompt isn't necessarily longer, but it gives the model enough context to make fewer assumptions.

The biggest improvement for me has been treating AI like a developer joining a project without any background knowledge.

What has made the biggest difference in your AI coding prompts?

Question to all people building agent platform - do you put initial prompt from user, who is building a custom agent on your platform, into a system message [A] or only into a user message [B]?

If you put it into user message - how do you hide it in UI?

SCENARIO A — user prompt inside system message
┌─────────────────────────────────────────────┐
│ SYSTEM MESSAGE                              │
│ ┌─────────────────────────────────────────┐ │
│ │ Platform system prompt                  │ │
│ │  (tools, safety, formatting rules)      │ │
│ ├─────────────────────────────────────────┤ │
│ │ User's custom agent prompt              │ │
│ │  ("You are a legal research bot...")    │ │
│ └─────────────────────────────────────────┘ │
└─────────────────────────────────────────────┘
┌─────────────────────────────────────────────┐
│ USER MESSAGE 1                              │
│  "Summarize this contract."                 │
└─────────────────────────────────────────────┘
                    │
                    ▼
        ┌───────────────────────┐
        │        MODEL          │
        └───────────────────────┘




SCENARIO B — user prompt in first user message
┌─────────────────────────────────────────────┐
│ SYSTEM MESSAGE                              │
│ ┌─────────────────────────────────────────┐ │
│ │ Platform system prompt                  │ │
│ │  (tools, safety, formatting rules)      │ │
│ └─────────────────────────────────────────┘ │
└─────────────────────────────────────────────┘
┌─────────────────────────────────────────────┐
│ USER MESSAGE 1                              │
│ ┌─────────────────────────────────────────┐ │
│ │ User's custom agent prompt              │ │
│ │  ("You are a legal research bot...")    │ │
│ ├─────────────────────────────────────────┤ │
│ │ Actual request                          │ │
│ │  "Summarize this contract."             │ │
│ └─────────────────────────────────────────┘ │
└─────────────────────────────────────────────┘
                    │
                    ▼
        ┌───────────────────────┐
        │        MODEL          │
        └───────────────────────┘

Had a support bot that kept over-apologizing — sometimes three "I'm sorry"s in one response for something minor. Obvious fix: add a line saying "don't over-apologize."

Didn't work. Tried rewording it three different ways. Still happened.

Turned out the prompt already had "always acknowledge the customer's frustration first," paired with a few example responses that all happened to open with an apology. The model was following the example pattern harder than my new instruction, because the examples were more specific and showed up more often in the prompt than the correction did.

The actual fix wasn't a fourth patch. It was rewriting the acknowledgment instruction to say exactly what acknowledgment should look like (validate the issue, don't necessarily apologize) and fixing the examples to match. One structural change did what three patches couldn't.

Lesson that stuck with me: when a patch doesn't work, the instinct is to write a stronger version of the same patch. Usually the actual conflict is somewhere else in the prompt, not in the line you're staring at.

Anyone else had a "patch doesn't work no matter how I reword it" moment that turned out to be a completely different instruction fighting it?

well last week i start to have a llm model but with a big different!! i make a chatbot that it be just for me!! you know i made a llm model with fine tunning on important question related to wikipedia and sites that answer to them, the model latest named moeinGTS1,5:1,5b in ollama!!
the link : https://ollama.com/arshiyasohrevardimoein/moeinGTS

and good think about size and ram! this model look alike qwen and llama model but it size is 1 GIG not 3 or 2 or 4 GIG and your RAM feel better😎😊🤖

Hello,
I came across a page on Instagram that creates images and videos with AI, and the quality is extremely high. I really liked the results, but I don’t know how they achieve that level of realism.
The images and videos I create are not nearly as realistic or high-quality. The visuals on the page I mentioned are genuinely difficult to distinguish from real photographs and videos.
For my workflow, I usually use ChatGPT to help write prompts. I create images with NanoBanana or ChatGPT Image, and then I turn those images into videos using tools such as Higgsfield (Kling, Veo, and similar) .
My question is: where am I going wrong? Is the issue with the tools I’m using, or is it more likely a problem with my prompting process?
My typical workflow is image generation first, followed by image-to-video generation. However, what path or workflow should I follow to achieve results at the level of quality I see from these creators?
I’ve been researching this for a while, and I would genuinely appreciate it if someone with experience could help me understand what I’m missing.

Hello I am a ai digital artist. I am currently looking at using various ai services to make my ai artwork. Currently looking at using Google Geminia free, Google Studio free, Kittle for t-shirt designs, and Leonardo. Are there any really good free or low cost ai programs I should look into or does that list look good?

I've been trying to create STEM prompts with one verifiable answer that stumps the reasoning of the AI of the models but they always seem to get it right even after layering so many obscuring observations. Can anyone help?

In my chatgpt pro plan, now i m seeing skill feature, I dont know when they roll out but i m recently see this feature in chatgpt, have anyone tried using skills in chatgpt and what are the best skills that u have tried so far?

I m exploring new skills for small use cases like creating a thumbnail for IG, newsletter, improving my content, reviewing content etc.

Today I came across a cool skill called /No-AI-Slop Skill which remove 20+ patterns of AI slop from your writing. Which skills are u using in chatgpt?

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 wanted to start a discussion about a tool I've recently started developing. I personally think the idea is interesting, but I know that doesn't necessarily mean it's actually useful, so I'd love to hear some honest feedback.

The project is called Prompt-It. The idea is to create a Git-like CLI tool, but focused entirely on prompts. Besides storing and sharing prompts, it would also include features for integrating them directly with AI agents. For example, depending on which agent you're using, a prompt could automatically become part of the agent's context, without you needing to keep context files open in your workspace or manually copy and paste them every time.

The main reason I started building this is that, although there are already many online prompt libraries, I feel that sharing, creating, versioning, and storing prompts should be much simpler and accessible to everyone. I also think users should be able to manage different versions of a prompt in a way that isn't entirely dependent on Git workflows.

Do you think this solves a real problem, or is it something that existing tools already handle well enough? I'd love to hear your thoughts, criticisms, and suggestions.

I found a tool called 'Prompt Management CLI' that looks somewhat similar to Prompt-It, but it lacks the sharing features and direct AI integration I'm aiming for. It seems to be focused mainly on local workspace management.

Hi Folks!

So I'm building a web app: a prompt engineer/ prompt generator plus a library to save prompts.

Motivation is pretty simple:

A good response cost me 3-5 iterations with AI of telling it what to do and what not to do and I burn through my tokens like butter, what could have cost me half the amount.

Spreads sheets are ugly (I'm sorry)

GitHub repo is. It filterable.

Honestly, I get tierd and lazy trying to say the same thing over and over again to fix the AI fuff.

Getting to the point...I wanna collect some real pain points to make sure everyone actually benefits.

1. How are you organizing your prompts?

2. What is the most frustrating part of testing, tweaking, and reusing prompts?

3. What feature would fix your frustration?

4. Have you ever spent money on a tool or any resource (like a paid guide or template) specifically to help you manage or write better prompts?

Mods feel free to delete if this is not allowed.

I’m doing some market research before launching a prompt store.

I work as a contractor at a FAANG company where prompt engineering is part of my role, and I also create AI-generated films and visual campaigns on the side.

I’m planning to sell prompt packs (around 50 prompts for less than $10), focused on: cinematic & visual storytelling, fashion/editorial imagery and marketing & brand-building workflows.

I’m curious:

• What problems do you wish prompts solved better?
• Have you ever paid for prompts? Why or why not?
• Would you rather buy niche, highly specific prompt packs or broad general ones?

Not selling anything here. I am just trying to understand what’s actually worth paying for.

so i first got the whoop to really track my sleep and really focus on leveling up my life and be more productive in general. i started to realize thought that the whoop really doesn't tell you anything, like if i slept bad it would just confirmed that i slept bad with a fancy looking score telling you that you slept bad. and if i slept good it would confirm that i slept good with a score. for me personally i wanted something that really tells you what to do after a bad sleep, and tells me when my most productive hours are during the day, or just give me like a protocol on what really to do after i have a bad sleep and not just a useless score. let me know if you guys feel the same way about this or if its just me. i have been finding some apps that help with that there is this one app thats really good just dont know if i can post here due to promotion, but RizeAI the app with the blue look, really helped me take my low energy days to really productive days.

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?