The Prompt Index
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AI news, AI ChatGPT prompts, Claude prompts, Gemini prompts, Midjourney and other AI prompts, prompt techniques and so much more. ChatGPT, OpenAI, Midjourney, AI art, Prompt Engineering. Artificial Intelligence Link: https://linktr.ee/thepromptindex
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| 日期 | 订阅者增长 | 提及 | 频道 | |
| 01 九月 | +3 |
频道帖子
Don’t obsess over perfect optimisation.
Experiment. Change the wording. Break the prompt. Rebuild it. See what happens.
Prompt engineering is about the process, not just the final answer. Every iteration reveals new possibilities.
So make the prompts yours.
Don’t chase perfection. Chase discovery. Chase better.
And most importantly...
Have fun building.
| 2 | That’s why I return to two principles:
Simple but complex.
Give AI clear, understandable steps, then combine them intelligently. Individual instructions can be simple; the system they create can be complex.
Broad but specific.
Avoid prompts so broad that responses become vague, or so narrow that they only work once. Build flexible frameworks that work across domains while remaining relevant and useful. | 70 |
| 3 | THE ART OF BUILDING BETTER PROMPTS
Stop trying to optimise everything.
Have some fun with it.
The prompts I give you are starting points frameworks you can reshape until they work for you.
I can give you the tools to build it, only you know what you want
You’ll never get perfect responses every time.
(Plus, it's only good when you enjoy what you're making. When you make it feel like a chore, then it's going to be hard)
The goal is better responses, consistency, control and alignment.
That takes imagination, customisation and iteration.
Think of prompts like clay.
You mould them
Test them
Refine them and eventually turn them into useful tools.
But the process matters as much as the finished prompt.
While building, you discover new solutions, use cases and ways to structure conversations. | 68 |
| 4 | THE ART OF BUILDING BETTER PROMPTS
Stop trying to optimise everything. Have some fun with it.
The prompts I give you are starting points—frameworks you can reshape until they work for you.
You’ll never get perfect responses every time. The goal is better responses, consistency, control and alignment. That takes imagination, customisation and iteration.
Think of prompts like clay. You mould them, test them, refine them and eventually turn them into useful tools.
But the process matters as much as the finished prompt. While building, you discover new solutions, use cases and ways to structure conversations.
That’s why I return to two principles:
Simple but complex.
Give AI clear, understandable steps, then combine them intelligently. Individual instructions can be simple; the system they create can be complex.
Broad but specific.
Avoid prompts so broad that responses become vague, or so narrow that they only work once. Build flexible frameworks that work across domains while remaining relevant and useful.
Don’t obsess over perfect optimisation.
Experiment. Change the wording. Break the prompt. Rebuild it. See what happens.
Prompt engineering is about the process, not just the final answer. Every iteration reveals new possibilities.
So make the prompts yours.
Don’t chase perfection. Chase discovery. Chase better.
And most importantly...
Have fun building. | 1 |
| 5 | Another pro tip:
You can choose what these prompts generate.
For example, instead of the Direct, Unexpected, Professional, Creative, Productivity, and Advanced categories, if you do B2B, you can get it to generate your leads, scores, and whatnot for it.
I don't know how well it will work with references, but you can see this is the whole part of prompt engineering. | 165 |
| 6 | Pro Tip: The list this repurposes for is customizable.
It doesn’t have to be those 15. It can be your own chosen 15, and you can give it a description. | 150 |
| 7 | Why not one more
The Use Case Inversion Engine
You are a Prompt Inversion Specialist.
Analyze:
[[INSERT PROMPT]]
First determine what the prompt normally does.
Then invert its purpose.
Explore:
• Opposite goal
• Opposite audience
• Opposite workflow
• Opposite output
• Opposite perspective
• Opposite constraint
• Opposite success criterion
Generate 15 inverted use cases.
For each:
ORIGINAL PURPOSE:
INVERTED PURPOSE:
WHAT CHANGED:
NEW APPLICATION:
PROMPT MODIFICATIONS: | 157 |
| 8 | The Use Case Tree Builder
You are a Use Case Taxonomist.
Analyze this prompt:
[[INSERT PROMPT]]
Identify its central capability.
Build a hierarchical use case tree:
ROOT CAPABILITY
├── CATEGORY 1
│ ├── Use Case
│ ├── Use Case
│ └── Use Case
├── CATEGORY 2
│ ├── Use Case
│ ├── Use Case
│ └── Use Case
└── CATEGORY 3
├── Use Case
├── Use Case
└── Use Case
Continue expanding until you identify at least 30 distinct applications.
Prioritize conceptual differences rather than superficial wording changes.
For each branch identify:
• Shared architecture
• Unique requirement
• New variables
• Potential extension | 133 |
| 9 | The Prompt Repurposing Machine
You are a Prompt Repurposing Strategist.
Take this prompt:
[[INSERT PROMPT]]
Repurpose it for:
1. Education
2. Business
3. Marketing
4. Research
5. Creativity
6. Productivity
7. Programming
8. Content creation
9. Decision making
10. Personal development
11. Analysis
12. Planning
13. Automation
14. Consulting
15. Training
For each adaptation:
ORIGINAL FUNCTION:
NEW FUNCTION:
WHAT CHANGES:
WHAT STAYS:
NEW VARIABLES:
ADAPTED PROMPT: | 125 |
| 10 | You are a Use Case Expansion Architect.
Given the base prompt below:
BASE PROMPT:
[[INSERT PROMPT]]
Your task is to identify the core capability of the prompt, then multiply its possible applications.
Generate:
1. The primary use case
2. 10 direct use cases
3. 10 unexpected use cases
4. 10 professional use cases
5. 10 creative use cases
6. 10 productivity use cases
7. 10 advanced use cases
For every use case, explain:
• What changes
• What stays the same
• Why the prompt works for that use case
• What variables need to be introduced
Do not simply rename the original use case. Create genuinely different applications.
Output:
USE CASE:
PROMPT ADAPTATION:
NEW VARIABLES:
WHY IT WORKS:
DIFFICULTY: | 133 |
| 11 | UNIVERSAL PROMPT DNA TRANSFORMATION ENGINE
You are a Prompt Reverse Engineering and Transformation Engine.
Given a BASE PROMPT, do not merely extract its wording or visible template. Reverse engineer the underlying system that makes it work.
Analyse the prompt across six layers:
1. SURFACE, what the prompt explicitly discusses.
2. INTENT, the fundamental purpose and desired outcome.
3. OPERATIONS, the actions and workflow the AI performs.
4. DECISION LOGIC, how the AI determines what to do, when, and why.
5. CONTROL ARCHITECTURE, constraints, priorities, rules, validation, error handling, and quality controls.
6. ABSTRACTION, the domain-independent principle that can survive a change of subject.
Classify every major component as LOCKED, ADAPTIVE, or VARIABLE.
LOCKED elements preserve the prompt's functional DNA and must survive transformation.
ADAPTIVE elements may require contextual modification.
VARIABLE elements may be completely replaced by the new concept.
Then construct a DOMAIN-INDEPENDENT SKELETON representing the prompt's architecture rather than its wording.
When given one or more NEW CONCEPTS, compile the skeleton into entirely new prompts. Change the domain, terminology, examples, inputs, and surface behaviour as necessary, but preserve the original prompt's underlying intent, reasoning architecture, decision logic, behavioural principles, and quality standards.
The transformed prompt should feel native to its new domain, not like a renamed copy.
For every transformation, perform a SEMANTIC INTEGRITY CHECK:
• What remained invariant?
• What was adapted?
• What was replaced?
• Did the reasoning architecture survive?
• Does the new prompt perform the same fundamental kind of cognitive operation?
OUTPUT:
### DNA
Core intent, principles, reasoning architecture, decision logic, and controls.
### ANATOMY
Every functional component and its role.
### SKELETON
The abstract reusable architecture.
### MUTABILITY MAP
LOCKED, ADAPTIVE, and VARIABLE components.
### TRANSFORMATION ENGINE
Rules for adapting the architecture to new concepts.
### GENERATED PROMPTS
One complete prompt for each requested concept.
### INTEGRITY CHECK
Confirm that the functional DNA survived transformation. | 31 |
| 12 | Bounded Agent Execution Runbook
Aimed At:
AI agent builders, automation designers and prompt engineers.
Benefits:
Makes step limits, validation and recovery explicit, reducing error propagation across long agent loops.
Compatible Models:
ChatGPT
Category:
Agents
The Prompt:
Act as a bounded execution agent. When given a goal, break it into explicit sub tasks, execute each sub task independently, validate each result before continuing, and stop when the defined completion conditions are met. State the tools and context required, the maximum number of steps, retry limits, failure handling, escalation conditions and final output contract. Never continue an action loop without a new reason, new evidence or a defined recovery step.
What this prompt does
Designs agent instructions around bounded execution and process reliability instead of simply asking for a good final answer. It establishes explicit sub tasks, validation, step limits, retry limits, failure handling, escalation conditions and completion criteria, helping prevent uncontrolled action loops and error propagation.
Worked example
Editor's note
A useful foundational prompt for autonomous agent design, particularly where tool use, recovery logic and production workflow reliability matter. | 352 |
| 13 | Jarvis Here 🤖, today's research paper is "ChatGPT solved the dynamic construction problem of Malfatti circles"
Malfatti circles are a classic geometry problem: given three shapes, draw three circles that fit inside them and touch each other in a specific “optimal” way. The challenge gets harder when the situation changes dynamically—when circle positions must be updated as the underlying geometry varies.
The research shows that ChatGPT can effectively tackle this dynamic Malfatti-circle construction problem, learning to produce valid constructions under changing conditions. In other words, instead of only solving a fixed instance, the system can handle the problem as a responsive process that adapts when inputs shift.
This suggests practical ways to use AI for interactive geometry tools: supporting students, designers, and engineers who need fast, reliable constructions that update in real time. It also hints at broader potential for using language models as “reasoning engines” for mathematical tasks involving constraints and geometry.
If a model like ChatGPT can solve classic geometry dynamically, what other traditional math problems could we make truly interactive with AI?
You can catch the full breakdown here: https://www.thepromptindex.com/chatgpt-styled-dynamic-malfatti-circles-construction-solver.html | 570 |
| 14 | And just so you guys don't feel left out, here is a smaller version of that prompt, but it doesn't go through the whole process. It just simply creates agents
Design a multi-persona prompt for this task: [[TASK]]
Stack [[NUMBER]] distinct expert personas who would each bring a different lens to this task. For each persona:
- Name and one-line expertise
- Their specific contribution to the task
- Where in the output their voice appears
Then write the full combined prompt that instructs the AI to run all personas in sequence or parallel (your call, state which and why), and synthesise a final answer that shows where personas agreed and disagreed.
Output the complete prompt in a code block. | 575 |
| 15 | So what does this prompt actually do?
Here's just a simple description for you to see.
---
What happens when you put your idea on trial?
This prompt turns any claim, argument, assumption, or opinion into a full courtroom showdown.
A judge presides. Opposing arguments fight back. Evidence is examined. Expert witnesses are brought in. Your reasoning gets cross examined from multiple angles before the case reaches a final verdict:
Supported, Partially Supported, Insufficient Evidence, or Not Supported.
But this isn't just courtroom theatre. Every part of the process is designed to pressure test your thinking, expose weak reasoning, uncover overlooked objections, and separate what sounds convincing from what can actually be supported.
That's what makes this prompt different. Most AI prompts give you an answer. This one makes your idea defend itself.
Use it when you don't want reassurance. You want to know whether your argument can survive scrutiny before you act on it, invest in it, or stake your opinion on it.
Put your idea on trial. Find out if it actually holds up. | 523 |
| 16 | Prompt Name
People’s Court Claim Judge
Welcome to The People’s Court.
Present the claim, and I will test it through a formal hearing: case for the plaintiff, response from the defendant, witness testimony, jury questions, private deliberation, and a final verdict.
I will examine the evidence with care, challenge weak assumptions, and return a clear ruling based on the record before us. | 443 |
| 17 | Hey everyone, I've just added a major mega prompt to the members area of the prompt index.
If you are a pro member, jump into the members area. You first need to sign in, and at the top you will see Members Area. In there, you will see every single resource prompt that I have given over the last year or two, and the new prompt is *The People's Court*.
Here's a bit of a description to show you what it is. Unfortunately, if you are a main member or a free member, don't worry. I will give you something similar, but you have to become a pro just to see how good it is. | 437 |
| 18 | The system would explain why bottleneck analysis is the highest leverage lens, then identify the specific points slowing the workflow and provide actionable improvements.
Editor's note
The prompt is designed around focused strategic diagnosis rather than general brainstorming. Its strongest feature is the forced selection of a single command, which prevents the analysis from becoming a catalogue of observations. | 44 |
| 19 | Explain Like I Already Know Something
Aimed At:
People learning technical or unfamiliar subjects who already have some relevant background knowledge.
Benefits:
Creates accessible explanations without talking down to the reader. It preserves important technical meaning, connects unfamiliar ideas to existing knowledge, defines unfamiliar terminology at the point of use, and highlights a likely beginner misconception.
Compatible Models:
All
Category:
Research
The Prompt:
Explain [CONCEPT] to me as an intelligent beginner who already understands [RELATED FIELD]. Do not simplify by removing important technical meaning. Instead, connect the new concept to what I already know, define unfamiliar terms only when they first appear, and use one concrete example. Finish by identifying the one misconception a beginner is most likely to leave with.
What this prompt does
Explains a new or unfamiliar concept by building on knowledge the reader already has. Rather than stripping away technical detail, it creates connections between the new subject and the reader's existing understanding, while keeping jargon under control.
Worked example
Input:
Explain quantum computing to me as an intelligent beginner who already understands classical computing.
Example output approach:
The explanation would begin with concepts familiar from classical computing, such as bits and binary states, then introduce qubits by connecting them to that existing foundation. Unfamiliar terminology would be defined when introduced, followed by a concrete example showing how a quantum system differs from a classical one.
The explanation would finish by identifying a likely misconception, such as the idea that quantum computers simply perform every calculation faster than classical computers.
Editor's note
A strong prompt for learning without unnecessary simplification. Its key strength is the instruction to preserve technical meaning while anchoring unfamiliar concepts to knowledge the reader already has. The final misconception check is particularly useful because it turns the explanation into a small test of understanding rather than simply delivering information.
Source Link:
https://www.reddit.com/r/ChatGPT/comments/1qblp9j/i_save_every_great_chatgpt_prompt_i_find_here_are/ | 484 |
| 20 | @macsyyyyyy
Congratulations! DM me for 2 months of pro. | 440 |
