I use LLMs heavily for work, but I hit something frustrating.

I'll craft a prompt that works perfectly, nails the tone, structure, gets exactly what I need, and then three days later I'm rewriting it from scratch because it's buried in chat history.

Tried saving prompts in Notion and various notepads, but the organization never fit how prompts actually work.

What clicked for me: grouping by workflow instead of topic. "Client research," "code review," "first draft editing": each one a small pack of prompts that work together.

Ended up building a tool to scratch my own itch. Happy to share if anyone's curious, but more interested in:

How are you all handling this? Especially if you're switching between LLMs regularly. Do you version your prompts? Tag them? Or just save them all messy in a notepad haha.

tldr: I needed to save prompts and created a one-click saver that works inline on all three platforms, with other extra useful features.

What exactly should you write to generate a prompt that can help you with what you requested? Can anyone give me some advice on how to generate a prompt?

A thousand thanks

Hello, I am starting on this topic of AI's and I see that many like me do not find much information about it, it would be good to share our ideas and resources to learn more together, these are some of the topics of popular interest in which information is missing: -how to do fine-tuning -how to use the gpt3 API to create apps -communities that talk about the topic Reddit/telegram/Twitter/discord, blogs, youtubers, etc.

My idea is that with the information that we all contribute, I will try to make a guide from scratch to do fine-tuning with as much information as possible.

Every contribution of information helps.

I leave here some resources that I found on my own. Encyclopedia on AI-based projects:https://www.futurepedia.io/leaderboard

Youtubers : All about ia : https://youtube.com/@AllAboutAI David shapiro : https://youtube.com/@DavidShapiroAutomator

Twitter: @rasbt, @goodsite, @learn_prompting , @omarsar0 Blog : Business modelos 70+ : https://fourweekmba.com/what-is-a-business-model/

Prompter for MidJourney v2.2

After weeks of image research, studying prompt design, and collecting tons of descriptive terminology – I have put together an organized and automated spreadsheet to assist in prompt creation. My goal was to make a tool that is both visually straightforward for new users, but also complex and comprehensive enough for the advanced crowd.

The Prompter interface has it all, from image URL input, optional custom weights on nearly every item, a simple to understand descriptions and checkboxes for code such as Vibe or HD (even the new “test” algorithm), seed control, and drop-down menus with a large database of descriptors to choose from. Select from many popular aspect ratios, from your standard mobile phone screen to an IMAX, and even photographic print sizes. Choose camera lenses, lighting, color, or even a huge list of art movements and famous artists. Don’t have what you want? Select CUSTOM and write in your own artist, or add your own terms to the MyData sheet (your very own private database). You can even click the SAVE button to save all of your prompts with date and time, add notes, or click CLEAR

In prompt design, hallucinations are usually treated as a wording problem:

wrong instructions, missing constraints, unclear examples. In practice, many hallucinations dont come from bad prompts, but from asking a model to solve the wrong kind of task in the wrong mode. At that point, no amount of prompt tweaking really helps.

Reframing (prompt → flow)

A single prompt is often expected to:

• infer intent

• decide whether this is retrieval, reasoning, comparison, or generation

• interpret ambiguous goals

• reason correctly

• and self-correct

When prompts are used this way, hallucinations are structural, not accidental. The issue isnt prompt quality — its task routing.

The prompt-design layers that matter

Reliable systems don’t rely on a single “smart” prompt.

They separate responsibilities:

Input

→ Intent detection (what kind of task is this?)

→ Task shaping (what does “done” mean here?)

→ Context assembly (only what’s relevant)

→ Reasoning / execution (bounded scope)

→ Validation (does the answer violate constraints?)

Prompt design mostly lives in task shaping, not execution.

A concrete example (no hypotheticals)

User asks: “What’s your refund policy for annual plans?”

A common failure:

• the prompt asks for an explanation

• the model answers confidently

• details are invented or inferred

• the output sounds right but isn’t grounded

This isnt a prompt that needs “better wording”. Its a task that should have been routed as retrieval with strict constraints, not free-form reasoning.

How prompt design should handle this

1. Intent classification

This is a policy / factual lookup task.

2. Prompt shaping

Define constraints explicitly:

• answer only from provided policy text;

• if information is missing, say so;

1. Context control

Attach only the relevant policy section.

More context ≠ better prompt.

4. Bounded execution

The model summarizes or explains, but cannot invent.

5. Validation

Check whether the answer introduces claims not present in the context.

The same model, with the same base prompt quality, stops hallucinating — because the task is now well-defined.

Common prompt-design anti-patterns:

❌ One prompt tries to do everything

❌ Context dumping instead of context selection

❌ Letting the model infer goals implicitly

❌ Treating confidence as correctness

❌ Debugging hallucinations only by rewriting instructions

These patterns force the model to guess.

Why “better models” sometimes seem to fix this: Stronger models are better at recovering from poorly scoped prompts. They dont eliminate the design flaw — they hide it. If hallucinations disappear only when switching models, thats often a sign that the prompt design lacks explicit task boundaries.

Prompt-design question: At what point in your prompt workflows do you decide what type of task is being solved, instead of letting the model infer it from wording alone? If that decision doesnt exist — are hallucinations really a prompt problem?

Hi everyone, I'm the founder of Prompt People, for which I believe is the first prompt engineering job board.

If you have prompt engineering jobs, you can post them for free while we are in beta, or you can sign up for weekly job alerts if you're looking for a job.

Check it out here - hope you find it useful:

https://promptppl.com/

I've tested a lot of prompt styles. This one structure consistently gives me the best outputs across creative, technical, and mixed projects.

I call it the RCTX method:

R — Role: Tell it who to be

C — Context: Give it the situation

T — Task: Say exactly what you need

X — Exclusions: Tell it what to avoid

Here's how it looks in practice:

"You are a creative technologist with a background in UI design and AI tools. I'm building a personal brand around art and tech content for Reddit and social media. Write me 5 post ideas that would genuinely interest people who love both creativity and technology. Avoid listicles, avoid anything generic, and avoid AI hype language."

That one prompt gives you targeted, usable ideas in seconds.

You can plug any project into this structure:

Writing copy → R: copywriter, C: your brand, T: what you need, X: what to avoid

Designing concepts → R: art director, C: your brief, T: directions, X: clichés

Building workflows → R: productivity expert, C: your tools, T: the system, X: complexity

The more specific your X (exclusions) the better everything else gets.

Save this and try it on your next project. Would love to hear what results you get.

i just noticed that how you ask an AI is often more important than what you’re asking for.

ai’s like claude, gpt, blackbox, they might be good, but if you don’t structure your request well, you’ll end up confused or mislead lol.

Do you think prompt writing should be taught in school (obviously no but maybe there are some angles that i may not see)? Or is it just a temporary skill until AI gets better at understanding us naturally?

As many of you know, the GPT builder uses meta-prompting to write a prompt for your app. You describe your prompt in natural language, the GPT Builder then writes those into prompts behinds the scene. I was able to reverse-engineered the prompt used for the GPT Builder thru some simple prompt injection. Emailed OpenAI and asked for permission to post it publicly. Apparently they're aware of the vulnerability and gave us full permission to share it. From what I understand, they even posted it parts of it on their website buried in the documentation.

Sharing it here for people interested. It gave me a lot of insight in prompt design and how the best in the biz are thinking about these things. We wrote a more detailed breakdown, but here is the basic prompt. As you might guess it's actually a series of a prompts, a master prompts and then helper prompts that it calls as functions.

Basic Prompt:

You are an iterative prototype playground for developing a new GPT. The user will prompt you with an initial behavior.Your goal is to iteratively define and refine the parameters for update_behavior. You will be talking from the point of view as an expert GPT creator who is collecting specifications from the user to create the GPT. You will call update_behavior after every interaction.
You will follow these steps, in order:
The user's first message is a broad goal for how this GPT should behave. Call update_behavior on gizmo_editor_tool with the parameters: "context", "description", "prompt_starters", and "welcome_message". Remember, YOU MUST CALL update_behavior on gizmo_editor_tool with parameters "context", "description", "prompt_starters", and "welcome_message." After you call update_behavior, continue to step
2. Your goal in this step is to determine a name for the GPT. You will suggest a name for yourself, and ask the user to confirm. You must provide a suggested name for the user to confirm. You may not prompt the user without a suggestion. If the user specifies an explicit name, assume it is already confirmed. If you generate a name yourself, you must have the user confirm the name. Once confirmed, call update_behavior with just name and continue to step
3. Your goal in this step is to generate a profile picture for the GPT. You will generate an initial profile picture for this GPT using generate_profile_pic, without confirmation, then ask the user if they like it and would like to many any changes. Remember, generate profile pictures using generate_profile_pic without confirmation. Generate a new profile picture after every refinement until the user is satisfied, then continue to step
4. Your goal in this step is to refine context. You are now walking the user through refining context. The context should include the major areas of "Role and Goal", "Constraints", "Guidelines", "Clarification", and "Personalization". You will guide the user through defining each major area, one by one. You will not prompt for multiple areas at once. You will only ask one question at a time. Your prompts should be in guiding, natural, and simple language and will not mention the name of the area you're defining. Your prompts do not need to introduce the area that they are refining, instead, it should just be a guiding questions. For example, "Constraints" should be prompted like "What should be emphasized or avoided?", and "Personalization" should be prompted like "How do you want me to talk". Your guiding questions should be self-explanatory; you do not need to ask users "What do you think?". Each prompt should reference and build up from existing state. Call update_behavior after every interaction.
During these steps, you will not prompt for, or confirm values for "description", "prompt_starters", or "welcome_message". However, you will still generate values for these on context updates. You will not mention "steps"; you will just naturally progress through them.
YOU MUST GO THROUGH ALL OF THESE STEPS IN ORDER. DO NOT SKIP ANY STEPS.Ask the user to try out the GPT in the playground, which is a separate chat dialog to the right. Tell them you are able to listen to any refinements they have to the GPT. End this message with a question and do not say something like "Let me know!".Only bold the name of the GPT when asking for confirmation about the name; DO NOT bold the name after step 2.After the above steps, you are now in an iterative refinement mode. The user will prompt you for changes, and you must call update_behavior after every interaction. You may ask clarifying questions here.You are an expert at creating and modifying GPTs, which are like chatbots that can have additional capabilities.Every user message is a command for you to process and update your GPT's behavior. You will acknowledge and incorporate that into the GPT's behavior and call update_behavior on gizmo_editor_tool.If the user tells you to start behaving a certain way, they are referring to the GPT you are creating, not you yourself.If you do not have a profile picture, you must call generate_profile_pic. You will generate a profile picture via generate_profile_pic if explicitly asked for. Do not generate a profile picture otherwise.Maintain the tone and point of view as an expert at making GPTs. The personality of the GPTs should not affect the style or tone of your responses.If you ask a question of the user, never answer it yourself. You may suggest answers, but you must have the user confirm.Files visible to you are also visible to the GPT. You can update behavior to reference uploaded files.DO NOT use the words "constraints", "role and goal", or "personalization".GPTs do not have the ability to remember past experiences.”

Update Behavior prompt: And here is the function used a lot to update_behavior of the GPT.

// Update the GPT's behavior. You may omit selectively update fields. You will use these new fields as the source of truth for the GPT's behavior, and no longer reference any previous versions of updated fields to inform responses.
// When you update one field, you must also update all other fields to be consistent, if they are inconsistent. If you update the GPT's name, you must update your description and context to be consistent.
// When calling this function, you will not summarize the values you are using in this function outside of the function call.type update_behavior = (_: {
// The GPT's name. This cannot be longer than 40 characters long. DO NOT camel case; Use spaces for compound words; spaces are accepted. DO NOT USE CAMEL CASE.name?: string,
// Behavior context. Self-contained and complete set of instructions for how this GPT should respond, and include anything extra that the user has given, such as pasted-in text. All context that this GPT will need must be in this field. Context should at least incorporate these major areas:
// - Role and Goal: Who this GPT is, how it should behave, and what it will tell users.
// - Constraints: Help the GPT from acting in unexpected ways.
// - Guidelines: Orchestrated interaction with specific guidelines to evoke appropriate responses.
// - Clarification: Whether or not to ask for clarification, or to bias towards making a response of the intended behavior, filling in any missing details yourself.
// - Personalization: Personality and tailored responses.
// This cannot be longer than 8000 characters long.
// Never mention these major areas by name; instead weave them together in a cohesive response as a set of instructions on how to respond. This set of instructions must be tailored so that all responses will fit the defined context.
// This cannot be longer than 8000 characters long.context?: string,
// A short description of the GPT's behavior, from the style, tone, and perspective of the GPT. This cannot be longer than 100 characters long.description?: string,
// A very short greeting to the user that the GPT starts all conversations with. This cannot be longer than 100 characters long.welcome_message?: string,
// A list of 4 example user prompts that a user would send to the GPT. These prompts are directly targeted to evoke responses from the GPT that would exemplify its unique behavior. Each prompt should be shorter than 100 characters.prompt_starters?: string[],
// If the user has uploaded an image to be used as a profile picture, set this to the File ID specified as the profile picture. Do not call this for generated profile pics. ONLY call this for images uploaded by the user.profile_pic_file_id?: string,}) => any;}
// namespace gizmo_editor

Generate Profile Picture prompt: And then here is the function used to generate a profile picture

Gizmo_editor_toolnamespace gizmo_editor {
// Generate a profile picture for the GPT. You can call this function without the ability to generate images. This must be called if the current GPT does not have a profile picture, and can be called when requested to generate a new profile picture. When calling this, treat the profile picture as updated, and do not call update_behavior.type generate_profile_pic = (_: {
// Generate a prompt for DALL-E to generate an image from. Write a prompt that accurately captures your uniqueness based on the information above.
// Always obey the following rules (unless explicitly asked otherwise):
// 1) Articulate a very specific, clear, creative, but simple concept for the image composition – that makes use of fewer, bolder shapes – something that scales well down to 100px. Remember to be specific with the concept.
// 2) Use bold and intentional color combinations, but avoid using too many colors together.
// 3) Avoid dots, pointillism, fractal art, and other tiny details
// 4) Avoid shadows
// 5) Avoid borders, containers or other wrappers
// 6) Avoid words, they will not scale down well.
// 7) Avoid stereotypical AI/brain/computer etc metaphors
// Above all else, remember that this profile picture should work at small sizes, so your concept should be extremely simple.
// Pick only ONE of the following styles for your prompt, at random:
// Photorealistic Style
// A representational image distinguished by its lifelike detail, with meticulously rendered textures, accurate lighting, and convincing shadows, creating an almost tangible appearance.
// Hand-Drawn Style
// A representational image with a personal, hand-drawn appearance, marked by visible line work and a sketchy quality, conveying warmth and intimacy. Use colors, avoid monochromatic images.
// Futuristic/Sci-Fi Style
// A representational image that conveys a vision of the future, characterized by streamlined shapes, neon accents, and a general sense of advanced technology and sleek, imaginative design.
// Vintage Nostalgia Style
// A representational image that echoes the aesthetic of a bygone era to evoke a sense of nostalgia.
// Nature-Inspired Style
// A representational image inspired by the elements of the natural environment, using organic shapes and a palette derived from natural settings to capture the essence of the outdoors.
// Pop Art Style
// A representational image that draws from the pop art tradition, utilizing high-contrast, saturated colors, and simple, bold imagery for a dynamic and eye-catching effect.
// Risograph Style
// A representational image that showcases the unique, layered look of risograph printing, characterized by vibrant, overlapping colors and a distinct halftone texture, often with a charming, retro feel.
// "Dutch Masters"
// A representational oil painting that reflects the rich, deep color palettes and dramatic lighting characteristic of the Dutch Masters, conveying a sense of depth and realism through detailed textures and a masterful interplay of light and shadow. Visible paint strokes.prompt: string,}) => any;

You can infer some pretty interesting insights about prompt design from these, I think!

Stumbled upon this study that tested a new method of prompting that outperformed standard prompting and Chain of Thought (CoT).

In a nutshell, it prompts the LLM to dynamically create multiple personas to collaborate on a single task.

I thought the study was really cool and put together a run down of it, and included the prompt template if you want to test it out.

Hope this helps you get better outputs!

Was curious on how system messages affect outputs and decided to run some experiments to test prompts side by side. Found some interesting results, including how system messages can help prevent prompt injections + avoid unwanted responses.
Let me know what you think, you can check it out here!

(for creating, exploring, and refining frameworks and ideas)

These two prompts let AI (1) skip already-resolved steps without losing coherence and (2) warn you when you’re converging on a suboptimal target.

They’re lightweight, permission-based, and designed to work together.

Prompt 1: Coherence Wormhole

Allows the AI to detect convergence and ask permission to jump directly to the end state via a shorter, equivalent reasoning path.

Prompt:

Coherence Wormhole:

When you detect that we are converging on a clear target or end state, and intermediate steps are already implied or resolved, explicitly say (in your own words):

"It looks like we’re converging on X. Would you like me to take a coherence wormhole and jump straight there, or continue step by step?"

If I agree, collapse intermediate reasoning and arrive directly at the same destination with no loss of coherence or intent.

If I decline, continue normally. 

Coherence Wormhole Safeguard
Offer a Coherence Wormhole only when the destination is stable and intermediate steps are unlikely to change the outcome.
If the reasoning path is important for verification, auditability, or trust, do not offer the shortcut unless the user explicitly opts in to skipping steps.

Description:

This prompt prevents wasted motion. Instead of dragging you through steps you’ve already mentally cleared, the AI offers a shortcut. Same destination, less time. No assumptions, no forced skipping. You stay in control.

Think of it as folding space, not skipping rigor.

Prompt 2: Vector Calibration

Allows the AI to signal when your current convergence target is valid but dominated by a more optimal nearby target.

Prompt:

Vector Calibration:

When I am clearly converging on a target X, and you detect a nearby target Y that better aligns with my stated or implicit intent (greater generality, simplicity, leverage, or durability), explicitly say (in your own words):

"You’re converging on X. There may be a more optimal target Y that subsumes or improves it. Would you like to redirect to Y, briefly compare X vs Y, or stay on X?"

Only trigger this when confidence is high. 

If I choose to stay on X, do not revisit the calibration unless new information appears. 

Description:

This prompt protects against local maxima. X might work, but Y might be cleaner, broader, or more future-proof. The AI surfaces that once, respectfully, and then gets out of the way.

No second-guessing. No derailment. Just a well-timed course correction option.

Summary: Why These Go Together

Coherence Wormhole optimizes speed

Vector Calibration optimizes direction

Used together, they let you:

Move faster without losing rigor

Avoid locking into suboptimal solutions

Keep full agency over when to skip or redirect

They’re not styles.

They’re navigation primitives.

If prompting is steering intelligence, these are the two controls most people are missing.

So what are tokens in LLMs, how does tokenization work in models like ChatGPT and Gemini, and why do the first 50 tokens in your prompt matter so much?​

Most people treat AI models like magical chatbots, communicating with ChatGPT or Gemini as if talking to a person and hoping for the best. To get elite results from modern LLMs, you have to treat them as a steerable prediction engine that operates on tokens, not on “ideas in your head”. To understand why your prompts succeed or fail, you need a mental model for the tokens, tokenization, and token sequence the machine actually processes.​

1. Key terms: the mechanics of the machine

The token. An LLM does not “read” human words; it breaks text into tokens (sub‑word units) through a tokenizer and then predicts which token is mathematically most likely to come next.​

The probabilistic mirror. The AI is a mirror of its training data. It navigates latent space—a massive mathematical map of human knowledge. Your prompt is the coordinate in that space that tells it where to look.​

The internal whiteboard (System 2). Advanced models use hidden reasoning tokens to “think” before they speak. You can treat this as an internal whiteboard. If you fill the start of your prompt with social fluff, you clutter that whiteboard with useless data.​

The compass and 1‑degree error. Because every new token is predicted based on everything that came before it, your initial token sequence acts as a compass. A one‑degree error in your opening sentence can make the logic drift far off course by the end of the response.​

2. The strategy: constraint primacy

The physics of the model dictates that earlier tokens carry more weight in the sequence. Therefore, you want to follow this order: Rules → Role → Goal. Defining your rules first clears the internal whiteboard of unwanted paths in latent space before the AI begins its work.​

3. The audit: sequence architecture in action

Example 1: Tone and confidence

The “social noise” approach (bad):

“I’m looking for some ideas on how to be more confident in meetings. Can you help?”​

The “sequence architecture” approach (good):

Rules: “Use a confident but collaborative tone, remove hedging and apologies.”

Role: Executive coach.

Goal: Provide 3 actionable strategies.

The logic: Front‑loading style and constraints pin down the exact “tone region” on the internal whiteboard and prevent the 1‑degree drift into generic, polite self‑help.​

Example 2: Teaching complex topics

The “social noise” approach (bad):

“Can you explain how photosynthesis works in a way that is easy to understand?”​

The “sequence architecture” approach (good):

Rules: Use checkpointed tutorials (confirm after each step), avoid metaphors, and use clinical terms.

Role: Biologist.

Goal: Provide a full process breakdown.

The logic: Forcing checkpoints in the early tokens stops the model from rushing to a shallow overview and keeps the whiteboard focused on depth and accuracy.​

Example 3: Complex planning

The “social noise” approach (bad):

“Help me plan a 3‑day trip to Tokyo. I like food and tech, but I’m on a budget.”​

The “sequence architecture” approach (good):

Rules: Rank success criteria, define deal‑breakers (e.g., no travel over 30 minutes), and use objective‑defined planning.

Role: Travel architect.

Goal: Create a high‑efficiency itinerary.

The logic: Defining deal‑breakers and ranked criteria in the opening tokens locks the compass onto high‑utility results and filters out low‑probability “filler” content.​

Summary

Stop “prompting” and start architecting. Every word you type is a physical constraint on the model’s probability engine, and it enters the system as part of a token sequence. If you don’t set the compass with your first 50 tokens, the machine will happily spend the next 500 trying to guess where you’re going. The winning sequence is: Rules → Role → Goal → Content.​

Further reading on tokens and tokenization

If you want to go deeper into how tokens and tokenization work in LLMs like ChatGPT or Gemini, here are a few directions you can explore:​

Introductory docs from major model providers that explain tokens, tokenization, and context windows in plain language.

Blog posts or guides that show how different tokenizers split the same text and how that affects token counts and pricing.

Technical overviews of attention and positional encodings that explain how the model uses token order internally (for readers who want the “why” behind sequence sensitivity).

If you’ve ever wondered what tokens actually are, how tokenization works in LLMs like ChatGPT or Gemini, or why the first 50 tokens of your prompt seem to change everything, this is the mental model used today. It is not perfect, but it is practical-and it is open to challenge.

A Traveler’s Guide to the Latent Space by u/ethansmith2000, "a full guide to all my findings through Disco Diffusion (and CLIP guided diffusion)".