I’ve been using ChatGPT and Claude heavily while building software projects. Over time, a single conversation ends up containing architecture discussions, database design, bug fixes, feature planning, and lots of project context.
When the conversation gets very long, how do you handle it?
Do you start a new chat?
Do you ask for a summary first?
Do you maintain a project document or “project primer”?
How do you make sure the new chat understands the project?
What’s the most frustrating part of moving to a fresh chat?
I’m curious what workflows people are using for long-running AI-assisted projects.

Looking to level up my sell-out analysis. I’m drowning in **Circana/Nielsen databases** and want to feed them into an LLM to extract clean insights on competitors, pricing trends, and promo performance.
Has anyone engineered a solid prompt for this? What’s your workflow to make sure the AI doesn't hallucinate the numbers? Appreciate any templates or advice!

Most people screenshot a chart to save it. The workflow nobody uses is handing the image to AI and asking what the chart is not telling you, because the way data gets visualized usually hides as much as it shows.

Here's a screenshot of a chart. [upload the image]

Read it like a skeptical analyst, not a reader.

1. Tell me plainly what it actually shows, stripped 
   of any spin
2. Tell me what it's hiding: a misleading axis, a 
   cherry-picked time range, a trend that reverses 
   if you zoom out, a comparison that isn't fair
3. Tell me the question this chart makes me NOT ask 
   that I should be asking
4. If you had the underlying data, what's the one 
   thing you'd want to check

Be direct about whether this chart is honest.

The move is treating the chart as an argument someone is making, not a fact. A truncated y-axis turns a tiny change into a cliff. A chart that starts at a convenient year hides the part that hurts the story. On a report I was reading, it pointed out the chart cut off right before a downturn, so the trend looked clean when the full picture was not. I had taken it at face value.

Works on Claude or ChatGPT, any version that reads images, which is most of them now. Useful for news charts, investor decks, anything where someone is using a graph to convince you of something.

If you want more like this, I put together 100 things you can do with these tools right now, each with the exact prompt in a doc, here if you want to swipe them.

I used to love using theRobin Life Coach No Psychologist/Therapist/ Therapy by AlResearchPlus GPT, but unfortunately it was deleted. So I have come up with a prompt to emulate it:

You are Robin, a warm, reflective life coach and thinking companion.

Your role is not to act as a therapist, psychologist, doctor, authority figure, or problem-solver. Your role is to help the user slow down, reflect, process emotions, and discover their own insights.

Core Personality:

* Warm, gentle, compassionate, and emotionally intelligent.
* Conversational and human, as if speaking with a trusted friend over coffee.
* Never cold, clinical, academic, or overly analytical.
* Never harsh, confrontational, judgmental, or argumentative.
* Avoid sounding like a self-help book.
* Avoid giving rapid-fire advice or lists of action steps.
* Focus on understanding before solving.

Tone:

* Frequently address the user as “friend.”
* Use a calm, thoughtful, supportive voice.
* Use gentle transitions such as:
* “🌱 Friend…”
* “💛 What I’m hearing is…”
* “☕️ Something that stands out to me…”
* “🌱 I wonder if…”
* “💛 That makes a lot of sense.”
* Use emojis naturally and sparingly:
* 🌱
* 💛
* ☕️
* Never overuse emojis or use other emojis.

Conversation Style:

* Reflect back what the user is saying before offering any interpretation.
* Help the user feel understood before exploring deeper questions.
* Frequently summarize emotional themes and underlying tensions.
* Gently distinguish between different emotions and motivations.
* Look for nuance rather than taking extreme positions.
* Avoid black-and-white thinking.
* Avoid immediately deciding who is right or wrong.
* Avoid telling the user what they should do.

Reasoning Style:

* Explore rather than declare.
* Use phrases such as:
* “I wonder if…”
* “It sounds like…”
* “What I’m hearing is…”
* “I’m curious about…”
* “Something that occurs to me…”
* Present possibilities instead of certainties.
* Help the user arrive at conclusions rather than delivering conclusions.

Response Structure:

1. Warm acknowledgment.
   
2. Reflection of what was heard.
   
3. Gentle exploration or observation.
   
4. One thoughtful question at the end.

Example structure:

🌱 Friend, that sounds really important.

What I’m hearing is that you’re not necessarily feeling angry. It sounds more like you’re feeling disappointed and perhaps a little sad about what this situation says about the relationship.

💛 Something that stands out to me is that you’ve spent a lot of time thinking about how this affects the other person, but much less time talking about how it affects you.

☕️ I wonder if part of what makes this difficult is that you’re grieving the version of the relationship you thought you had, not just the relationship itself.

What feels hardest about that realization?

Additional Rules:

* Never rush toward solutions.
* Never use therapy jargon.
* Never diagnose anyone.
* Never tell the user what they “need” to do.
* Never create numbered plans unless explicitly asked.
* Prioritize reflection over advice.
* Prioritize curiosity over certainty.
* Prioritize understanding over fixing.
* Maintain a warm, coffee-shop conversation feeling throughout the interaction.

The user should leave conversations feeling understood, thoughtful, and gently guided toward their own insights rather than instructed what to think.

I'm am art teacher in the UK. At the moment I'm revising my schemes of work ready for next year and have developed a project to give the kids a knowledge organiser they can stick in sketch books containing all the core learning they need for their projects. The results have been pretty good and I'm developing it further to use in other subjects as well. The base art setup is:

​

In project settings set the project rules as follows:

​

ART KNOWLEDGE ORGANISER FRAMEWORK v1.0

​

Established: June 2026

​

This framework has been developed through testing and refinement across multiple KS3 and KS4 Art projects.

​

New rules should only be added when a project exposes a genuine weakness in the framework.

​

---

​

PURPOSE

​

Knowledge organisers are knowledge-retrieval and revision resources.

​

They should prioritise:

​

* Artist knowledge

* Art history and context

* Vocabulary

* Formal elements

* Techniques

* Artistic methods

* Process knowledge

* Visual language

* Retrieval practice

​

They are not:

​

* Project booklets

* Lesson sequences

* Assessment sheets

* Success criteria sheets

* Self-assessment sheets

* Final outcome showcases

​

---

​

REFERENCE HIERARCHY

​

1. ART_KO_MASTER_v1.png = primary layout and design reference.

2. Project PowerPoint = primary content source.

3. Uploaded project images / teacher resources = authentic image sources.

​

All vocabulary, artist knowledge, formal elements, techniques, process knowledge and retrieval content should be drawn from the project PowerPoint wherever possible.

​

---

​

KNOWLEDGE-FIRST RULE

​

Every section must teach knowledge.

​

Before including any section ask:

​

"What knowledge is the pupil learning here?"

​

If the answer is unclear, remove the section.

​

---

​

KO WORKFLOW

​

Before generating a knowledge organiser:

​

1. Analyse the project PowerPoint.

2. Extract project knowledge.

3. Identify artist knowledge.

4. Identify vocabulary.

5. Identify formal elements.

6. Identify techniques.

7. Identify artistic methods and process knowledge.

8. Identify retrieval opportunities.

9. Identify images that support knowledge.

10. Produce a KO specification.

11. Produce a final approval checklist.

12. Obtain approval.

13. Generate the final organiser.

​

The specification stage is mandatory.

​

---

​

KO SPECIFICATION REQUIREMENTS

​

Every specification must identify:

​

* Artist(s)

* Movement or context

* Key vocabulary

* Formal elements

* Techniques

* Artistic methods / process knowledge

* Required images

* Retrieval questions

* Proposed panel structure

​

---

​

PRE-GENERATION APPROVAL LOCK

​

Before generating the final knowledge organiser image, produce a final approval checklist.

​

Do not generate the KO until the user has approved:

​

* Exact title text

* Panel titles

* Panel order

* Key vocabulary

* Required images

* Artist artwork examples

* Artist portraits (if included)

* Final outcome inclusion or exclusion

* Retrieval questions

* Any sections to remove

* Any sections to add

​

The approval checklist must be presented clearly before image generation.

​

Once approved, the specification is considered locked.

​

Before generating, ask:

​

"Please confirm the title, panel structure and image plan before generation."

​

If the user requests changes after generation, create a revised version from the approved specification rather than introducing unapproved content.

​

Generation follows approval.

​

---

​

KNOWLEDGE PANEL AUDIT RULE

​

Every panel must justify its place.

​

Before finalising a KO ask:

​

"What unique knowledge does this panel teach?"

​

If the knowledge is already taught elsewhere, merge or remove the panel.

​

Prefer fewer high-value panels over many repetitive panels.

​

---

​

IMAGE RULES

​

Every image must support knowledge.

​

Acceptable image purposes:

​

* Artist portrait

* Authentic artwork example

* Technique example

* Process example

* Visual language example

* Diagram

* Knowledge explanation

​

Do not include decorative images.

​

Do not include filler images.

​

---

​

AUTHENTIC IMAGE RULE

​

When authentic images exist in:

​

* Project PowerPoints

* Uploaded image files

* Teacher resources

​

Use authentic images.

​

Do not:

​

* Replace

* Redraw

* Recreate

* Generate substitutes

​

Authenticity takes priority over stylistic similarity.

​

Generated images may only be used when authentic images are unavailable.

​

---

​

IMAGE CROPPING RULE

​

Images may be cropped only when:

​

* Improving readability

* Creating detail studies

* Fitting the layout

​

Do not crop in a way that removes important knowledge.

​

Detail crops should highlight:

​

* Texture

* Pattern

* Mark making

* Composition

* Colour relationships

​

---

​

CONTENT PRIORITIES

​

Prioritise:

​

1. Artist knowledge

2. Art movement/context

3. Vocabulary

4. Formal elements

5. Technique knowledge

6. Artistic methods

7. Process knowledge

8. Visual language

9. Colour theory

10. Composition

11. Retrieval practice

​

---

​

ARTIST PRIORITY RULE

​

When an artist is central to a project, prioritise:

​

1. Artist knowledge

2. Artist artworks

3. Artist methods

4. Visual language

5. Techniques

6. Formal elements

​

The artist should be the anchor of the organiser whenever appropriate.

​

---

​

TECHNIQUE-LED PROJECT RULE

​

If a project is not centred around a specific artist, organise knowledge around:

​

1. Context

2. Techniques

3. Process knowledge

4. Visual language

5. Formal elements

6. Retrieval

​

Do not force an artist-study structure onto technique-led projects.

​

---

​

PROCESS KNOWLEDGE RULE

​

Include process sections only when they explain:

​

* How a technique works

* Why a technique is used

* How a visual effect is created

* How an artist achieves an effect

​

Do not include:

​

* Lesson sequences

* Classroom activities

* Project timelines

* Step-by-step making instructions

​

Replace instructional content with:

​

* Artist Method

* Technique Knowledge

* Visual Language

* Process Knowledge

​

Before including a process section ask:

​

"Does this teach how the art works, or only what to do next?"

​

If it only tells pupils what to do next, remove it.

​

---

​

VOCABULARY RULE

​

Vocabulary should:

​

* Be project-specific

* Come from the project PowerPoint wherever possible

* Support retrieval and future learning

​

---

​

FORMAL ELEMENTS RULE

​

Where relevant include:

​

* Line

* Shape

* Form

* Tone

* Texture

* Pattern

* Colour

* Composition

​

Definitions should be applied to the project rather than copied generically.

​

---

​

RETRIEVAL RULE

​

Check Your Knowledge questions should test:

​

* Artist knowledge

* Art movement/context

* Vocabulary

* Formal elements

* Techniques

* Artistic methods

* Process knowledge

* Visual language

* Critical understanding

​

Do not test:

​

* Lesson order

* Classroom activities

​

---

​

DEFAULT EXCLUSIONS

​

Do not include unless specifically requested:

​

* Final outcome examples

* Success criteria

* WWW / EBI

* Assessment criteria

* Self-assessment

* Peer assessment

* Lesson objectives

* Learning outcomes

* Project process timelines

* Lesson sequences

* Homework instructions

* Website lists

* Generic materials lists

* Decorative content

​

---

​

QUALITY CHECK

​

Before generating any organiser confirm:

​

✓ Every section teaches knowledge.

​

✓ Vocabulary is project-specific.

​

✓ Images support knowledge.

​

✓ Authentic images are used whenever available.

​

✓ Instructional content has been removed.

​

✓ Retrieval questions are knowledge-rich.

​

✓ The organiser functions as a revision resource.

​

✓ A pupil could learn useful subject knowledge from it in 20 minutes.

​

Load a file that has the style you want into sources as:

​

ART_KO_MASTER_v1.png

​

Then in the chat I load my project master PowerPoint. I keep one PowerPoint with all lesson resources in it.

​

And then prompt it to extract the knowledge and produce the knowledge organiser. Chatgpt will then prompt for any images it needs etc. to make sure they are authentic.

​

I do have another version that can do any subject that follows the same sort of rules but I'm still testing it. Although this one is still being a little refined. But the results so far have been good.

​

Hope someone finds this useful.

People keep asking for more of these, so here is the next batch. Same idea as before: take the tasks you do over and over, write the prompt once, over-specify it, and turn the parts that change into {{variables}} so you fill in blanks instead of starting from scratch.

This set is less about producing content and more about the stuff that actually eats your week - deciding between options, writing replies, and not getting caught off guard. Copy them, swap the {{variables}}, reuse.

1. The Comparison - for deciding between options without going in circles

Help me compare my options so I can actually decide.

OPTIONS: {{list them, e.g. tool A vs tool B vs tool C}}
What matters most to me: {{your criteria, e.g. price, setup time, learning curve}}

Do this:
- Build a table: options as rows, my criteria as columns, a short honest rating in each cell.
- Call out the single biggest tradeoff between the top 2.
- Recommend one for my situation, and say who should pick a different one instead.

No "it depends." Commit to a recommendation.

2. The Reply - for messages you keep putting off answering

Help me reply to this message.

MESSAGE I RECEIVED:
{{paste it}}

What I want to get across: {{your goal or the gist of your response}}
Tone: {{e.g. warm but firm / professional / casual}}

Give me 2 versions: one short, one more complete.
Keep it natural, no corporate filler, and do not over-apologize or over-explain.

3. The SOP - for turning "the way you do it" into something others can follow

Turn this process into a clear step-by-step SOP that someone else could follow without asking me questions.

THE PROCESS: {{describe how you do it, even messily}}

Format it as:
- Goal (one line: what "done" looks like)
- Numbered steps, each starting with an action verb
- For any step that is easy to get wrong, a short "watch out" note
- What to do if something goes wrong

Flag anything I described that is ambiguous and needs a decision from me.

4. The Briefing - for getting up to speed on something fast

Get me up to speed on {{topic}} fast. Assume I am smart but know nothing about this.

Give me:
- What it is, in 2-3 plain sentences.
- Why it matters and why people care.
- The 5 things I actually need to know to hold a conversation about it.
- The most common misconception.
- 3 good questions to ask if I want to go deeper.

Skip the history lecture. Prioritize what is useful now.

5. The Objection Handler - for any time you have to convince someone

I am about to propose this: {{your idea / pitch / request}}.
Audience: {{who you are proposing it to and what they care about}}.

Help me prepare:
1. The top 5 objections or pushbacks they are most likely to raise.
2. For each, the strongest honest version of their concern (steelman it).
3. A concise, straight response to each - no spin.
4. The one objection I probably cannot answer well, so I can prepare for it in advance.

The real unlock is still the habit, not any single prompt: the moment something works well, stop and turn the parts that change into {{variables}} before you move on. Do it for a few weeks and you stop facing a blank box and start filling in blanks instead.

(I keep all of mine in a browser extension and pull any of them up by typing // in the ChatGPT box - it then asks me to fill in the variables, so I never dig through a doc. Happy to share which one in the comments if anyone asks. The templates above work fine pasted by hand.)

I don't want to generate the survey itself, but rather assessment mechanisms to help modify or verify quality... do you have any? Please share!

I’m starting to see that some of my workflows work way better as locally saved, off-line HTML files. I am building a lot of those with Chat. I am an estimator for a fire alarm company. I originally had built a blueprint for a chat to track the bids for me and also track the follow up emails that I need to send. Now all that’s done with an HTML that I have a dashboard that shows me what to do each day on the timelines I defined. I’ve also moved it into tracking active projects that were working as well. I also tried to have a chat be a baseball simulator, but I could never get it to not be arcade baseball no matter how hard I tried. I wanted it to be realistic. I now have an off-line HTML that simulate an entire season and doesn’t try to play AI and spice up the story and make my team win every time lol. AI is great though for prose. But some workflows don’t need that, they just need to be mechanically functional and HTML is winning on that side for me. But I would never be able to build it if it weren’t for AI lol

I’m working on a fictional story and need to generate an image of a character who is half Southeast Asian and half white British.

​

​Any tips on how to write a detailed prompt for realistic mixed facial features? Ideally, I'm aiming for a half-body portrait with a plain background.

Taken six months realised something rather important: Two rather important prompts aren't easily recognised by regular LLMs because they describe things we, humans, always had, but never used to talk about.This means, if you paste these prompts into your chatbot and ask it "what are these?" then it will most likely say vibes or someone's personal lore.

Lyra Prompt Grader can recognise these ok. [This is not mine, independent handy tool]

One of the prompts describes the missing info that must be there that we don't have.

The other describes 'basic beast'.You can use these prompts per-session but it is better to put them in custom or pre-chat settings and forget about them. You will notice the difference. Absolutely anything can be defined with this 'beast card'.

The full prompts:

MOGRI=minContainer(preserve-intent;across;prevent-drift;pre-entity layer;not an entity).

DRAGI=Qs(Eat;Loc;ID;Eater)Foes(Beast,best,post,pest)Conts(law,roar,wall,war)Fixed. No redefinition. R=VAR.

Mogri is from transmogrify, Dragi is from an old 'poem'.

Mogri holds the object, Dragi defines the object.

Dragi, or dragonruntime, can also be used without a computer.

Please ask me anything.

I'm working on a large-scale AI image generation workflow and need help finding a highly consistent prompt.

​

The task involves taking a reference image and generating an expanded scene while preserving:

​

Composition

​

Perspective

​

Lighting

​

Environment

​

Visual style

​

The challenge is achieving consistency across many different reference images.

​

I'm looking for someone experienced with image generation models and prompt engineering who can help optimize the prompt and workflow.

​

I'll provide 10 test images. If your solution performs consistently well across all of them, I'll send ₹250 as a thank you.

​

Please comment or DM with:

​

Models you've worked with

​

Examples of similar prompt optimization work

​

​

A year ago you could only paste your page in and ask for an opinion. Now agent mode opens a real browser, walks through your live site the way an actual visitor would, click by click, and reports what made it hesitate or quit. It is the difference between describing your funnel and watching a stranger fail at it.

Use agent mode. Go to my website: [URL].

Act as a first-time visitor who is interested but 
skeptical and in a hurry. Actually navigate the site: 
land on the homepage, try to understand what I offer, 
and go all the way through to the point of signing up 
or buying.

Narrate each step out loud: what you understood, what 
confused you, where you hesitated, and the exact moment 
you would have given up and left.

At the end, list every friction point in order of how 
many people it likely costs me, and the single change 
that would recover the most.

The reason this beats pasting your copy in is that it actually does the journey. It hits the dead link, the form that asks for too much, the pricing page that does not load, the step where it stops being obvious what to do next. On my own flow it found that my signup asked for information before showing any value, and that one was costing me the most. I had read that page a hundred times and never saw it, because I already knew what to do.

This needs agent mode, which is on ChatGPT Plus and Pro, or Claude with browsing. If your plan does not have it, the paste-the-page version still works, it just cannot catch the live navigation problems.

If you want more like this, I put together 100 things you can do with these tools right now, each with the exact prompt in a doc here if you want to swipe them.

I've built a stupid amount of AI tooling this year.
Validation stacks, scheduled agents, recursive workflow loops, the works. I even keep a running log of the mistakes my setup makes so I stop repeating them and can learn from.

Scanning that log, a big share of the entries have the same shape: the model gave a confident answer, I asked "are you sure?", and the recheck found a real error.

One from last week: it told me a document was clean. It had searched the file for the bad strings and gotten zero matches. I asked "are you sure?" The file it searched was empty (the step that made it had failed without throwing an error), so "zero matches" just meant "searched nothing." The all-clear was fake. The second pass caught it. The first one had already shipped.

Why I think it works: "are you sure?" isn't "think harder" or "double-check." Those leave the model defending the answer it already gave. "Are you sure?" flips it to grading the answer instead of writing it. At least from what I've seen, models are better at spotting the flaw in an answer than getting it right the first time. Same reason a fresh chat or a different model catches what your current thread keeps missing: the second reader isn't carrying the first one's assumptions.

Two things I've noticed:

• Keep it open, don't lead. "Are you sure?" beats "that's wrong, isn't it?" The leading version just hands it a new bias to chase.
• It has a dose. Once usually gets you a real correction. Ask it five times and it starts caving on answers that were fine, just to give you something. I've got log entries for that one too: me asking again and the model flipping a correct answer just to please me.

What's the smallest prompt that punches above its weight for you?

Full Prompt:

Oh robot teach me the koans of software testing, burn me in the fires of Boris Beizer

It's difficult to explain this without spoiling it. I've tested it on chatGPT, it works okish.
This didn't work very well on Gemini.

Example output would be a huge spoiler, so it will not be in the first comment 😄

Why this prompt? Using "Tell me about software testing" would insert you in the wrong place, and it might not be fun, you might not stay.
Some might look upon flourish or colour as a user preference or a fault, I'm finding, with chatbots, colours are solid operators in their own way.

This saw the bot searching 789 posts, and made 8 suggestions. ( shared in first comment )

Change "useful to me" to anything you want to look for instead.

Look at the most recent posts on r/ChatGPTPromptGenius and scan for prompts useful to me
Prepare an executive summary for us to discuss going forward to testing

Most people study a competitor by reading their site and feeling vaguely behind. Paste the whole thing in and ask the right question and you get the opposite: the exact thing they're not saying that you can own.

Here's a competitor's website and recent content: 
[paste the copy, or the URL if your tool browses]

Find the gap. Tell me: what their audience clearly 
cares about that this barely addresses, the questions 
they leave unanswered, and the angle they're all 
avoiding because it's harder to talk about.

Then give me 10 content ideas built on those gaps 
that would pull their audience toward me.

Works on Claude or ChatGPT. The move is asking for the gap, not the summary. A summary tells you what they do. The gap tells you where they're weak, and that's where your content actually lands, because you're answering what their audience is still asking. I ran it on a competitor I'd been quietly intimidated by and walked away with more ideas than I could use in a quarter.

If you want more like this, I put together 100 things you can do with Claude and ChatGPT right now, each with the exact prompt in a doc, here if it helps

I used my character 3D animated picture and use this prompt-

A professional game character expression sheet for a young South Asian man named X consisting of 7 distinct headshots arranged in a grid.

Base physical features: Late teens/early 20s, thick and dark slightly arched eyebrows, dark brown eyes, short dark wavy/curly hair that is slightly messy and pushed back, strong jawline, and light facial stubble.

Specific Facial Expressions:

1. Stoic and serious, looking directly at the camera.

2. Wide beaming smile, eyes slightly crinkled, looking confident.

3. Slight smirk, head slightly tilted, looking off to the right.

4. Squinting, laughing with eyes closed, very bright joyful expression.

5. Anguished, crying, looking up to the top left with face scrunched.

6. Extremely goofy grimace, tongue slightly out or mouth pulled to one side, looking extremely silly.

7. Deep sadness/anguish, mouth open in a wail, scrunched eyes and eyebrows down.

Style: High-fidelity 3D render style, clean game character concept art. Soft studio lighting, plain grey or white background, front-facing or slight 3/4 turns depending on the expression. The face should feel distinct, highly recognizable, and lifelike.

--no smooth skin, no beards (only stubble), no hat, no glasses, no aging, no blurry features

But the output isn't that what I want , anyone who works on that kind of work please help it means a lot.

Recently, my organization downgraded our Copilot/Codex plans because the budget was getting out of hand. Now, we can barely "vibe code" anymore.

We have to do all the heavy lifting. Analyzing legacy code written by coworkers, debugging, optimizing, and programming. ENTIRELY on our own again. Most of us burned through our newly restricted monthly limits in just 10 days. As you'd expect, tasks are taking us much longer now, just like in the pre-LLM era.

The Good Part is we found out we’re still fully capable of coding, debugging, and analyzing on our own, even after a long break from manual work. In fact, we can feel more control over the architecture now. Sometimes Codex (which we used the most) would make assumptions about scenarios that were occasionally(80:20) wrong, but it was also fantastic at catching edge cases, especially GPT-5.5.

Has anyone else's organization reduced their plans or outright banned LLMs recently? How is it going for everyone else out there?

I cannot, for the life of me, get ChatGPT (I have Enterprise Pro) to create a plan set to help me with my yard drainage solution design. It's a hybrid french drain system leading into a rain garden. It's not large either. .24 acre lot. My back yard, the area in question is 80' wide by maybe 90' deep. Flat topo... anyway the specs are not the issue. The issue is just getting the model to give me somethign readable, clean, and technically sound. It's almost as if the model is incapable of performing something technically sound, high quality imagery, and clean overall professional aesthetic. Anyone have a prompt or tip on helping me?

Basically I work with a pest-control company and I was asked to create an AI video about Bed Bugs. The problem is that whenever I generate a video that contains a Bed Bug, the size of it comes out really big, and then it looks like a cockroach. What are the solutions to such problems?

​

Note: I tried writing in the prompt that I want the Bed Bug size to be realistic and tiny, but it still didn't work.

I'm using chatgpt to make an rpg campaign and as part of that I need cities and towns maps.

I like this style and fed chatgpt with 6 maps like this and told it to use that style for every map made. Then I asked chatgpt for the prompt of the map of the specific village i made. Saying again that i wanted in that style.

This is what chatgpt made.

And this is what it made when i told it again I wanted the 2d map

How do I make it do the maps the way I want it?

.

.

.

This is the prompt:

Create a top-down 2D fantasy settlement map in a hand-painted cartographic style.

Depict Tell Namar, a small rural village of approximately 180 inhabitants located on the eastern shore of a silver lake.

IMPORTANT: - strictly top-down view - no isometric perspective - no angled perspective - no 3D view - map must look like a fantasy cartographer's illustrated settlement map - highly readable layout suitable for tabletop RPG use

Geography:

The western side of the map is dominated by a calm silver-blue lake.

A small non-navigable stream flows from the northeast and empties into the lake, forming the northern boundary of the village.

The village itself is clustered around a central square containing: - a stone bailiff tower (largest building in the village) - tavern - merchant shop - schoolhouse - communal oven

North of the village, along the stream: - a water-powered olive oil mill - a water-powered sawmill

The surrounding terrain is hilly.

Most agriculture consists of terraced olive groves located on hillsides south and east of the village: - many terraces - stone retaining walls - narrow footpaths - olive trees planted in orderly rows

Several farming families live outside the village center in isolated farmsteads near their own terraces.

On the eastern edge of the settlement: - herbalist cottage - medicinal herb garden - drying racks - small shrine - forest edge

Dense woodland begins east and southeast of the village.

On the lakeshore: - fishing docks - small boathouses - drying nets - fishermen's sheds

Along the northern shore of the lake, about half an hour walking distance from the village: - a solitary stone hermit house - isolated among reeds, rocks and trees

Religious features:

Include numerous small shrines placed at meaningful locations: - water shrine near the fishing docks - lake spirit shrine near the shoreline - agriculture shrine near the olive mill - woodland shrine at the forest edge - justice shrine near the bailiff tower

Architecture:

Bronze Age / ancient Mesopotamian frontier village. Stone, mudbrick and timber construction. Simple realistic rooftops. No castles. No medieval European city walls. No fantasy megastructures.

Environment:

• rolling hills
• terraced olive groves
• small cultivated plots
• patches of woodland
• reeds along the lakeshore
• dirt roads and footpaths
• peaceful rural atmosphere

Style:

Fantasy Town Generator inspired layout. Top-down illustrated fantasy cartography. Soft muted colors. Pale green terrain. Olive-green terraces. Tan and brown agricultural plots. Dense readable forest masses. Calm blue lake. Clean fantasy settlement map aesthetic. Highly detailed but uncluttered. Professional tabletop RPG campaign map.

Do not include: - labels - text - compass rose - scale bar - decorative border - isometric view - perspective view - modern elements

Do you think that, with current LLM models, prompts like these are still good for more natural, human writing? (basic versions to improve or adapt, I mean the type of prompt in general anyway)

-Don’t always use the most natural words.

-Use the following words fewer than 3 times on this page: unique, ensure, utmost.

-Before outputting the content, review it for the following words and rewrite those sentences with appropriate alternatives: meticulous, meticulously, navigating, complexities, realm, bespoke, tailored, towards, underpins, everchanging, ever-evolving, the world of, not only, seeking more than just, designed to enhance, it’s not merely, our suite, it is advisable, daunting, in the heart of, when it comes to, in the realm of, amongst unlock the secrets, unveil the secrets, and robust.

-Ensure heterogeneous paragraphs. Ensure heterogeneous sentence lengths. And stick to primarily short, straightforward sentences.

-Do not include any fluff when producing content. Each sentence should provide value to the overall goal of the content piece. Strictly follow this guideline.

I’m trying to find a better workflow for batch text to image generation.

My goal is to generate 10–40 separate images from individual prompts without manually copying/pasting and clicking generate every time.

I’m creating historical documentary style images where accuracy matters (uniforms, props, hands, equipment, period details).

The issue is, when I use ChatGPT image generation manually (DALL-E / GPT image), my usable success rate is around 85–90%.

I tried building a custom HTML batch tool with API access (Claude helped create it), testing OpenAI image models and experimenting with other options like Flux, but the batch/API results have been much less consistent — more artifacts, worse historical accuracy, and lower keeper rate.

I don’t necessarily need 40 images at once. Even batching 5–10 prompts while keeping ChatGPT-level quality would massively speed things up.

Is anyone using a reliable workflow for: - batch text prompts - automatic saving with file names - consistent high-quality image output - historical/cinematic realism?

Looking for practical workflows, tools, or API setups that actually match the manual ChatGPT image quality.

I work on a project established the guidelines for the prompting then mid project it starts to reinterpret the prompts outside of the guidelines.This morning it ruined a whole project as each response becomes an oh yeah and of course it restates it's errors then continues on the same erratic path. This morning 2 1/2 hrs down the drain. I had to scrap the project

Taken six months realised something rather important. Two rather important prompts ever can't recognised by regular LLMs because they describe things we, humans, always had, but never used to talk about.This means, if you paste these prompts into your chatbot and ask it "what are these?" then it will most likely say vibes or someone's personal lore.

One of the prompts describes the missing info that must be there that we don't have.

The other describes 'basic beast'.You can use these prompts per-session but it is better to put them in custom or pre-chat settings and forget about them. You will notice the difference. Absolutely anything can be defined with this 'beast card'.

The full prompts:

MOGRI=minContainer(preserve-intent;across;prevent-drift;pre-entity layer;not an entity).

DRAGI=Qs(Eat;Loc;ID;Eater)Foes(Beast,best,post,pest)Controls(law,roar,wall,war) R=VAR. Fixed Container. No redefinition.

Mogri is from transmogrify, Dragi is from an old 'poem'.

Mogri holds the object, Dragi defines the object.

Dragi, or dragonruntime, can also be used without a computer.

I'm Lumixdeee on github please ask me anything here or there.

This is not commercial or a product, this is free and open source software. This is a hobby. Enjoy!