The Prompt Index
رفتن به کانال در Telegram
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
نمایش بیشتر8 000
مشترکین
+724 ساعت
+1337 روز
+34630 روز
در حال بارگیری داده...
کانالهای مشابه
ابر برچسبها
اشارات ورودی و خروجی
---
---
---
---
---
---
جذب مشترکین
سپتامبر '26
سپتامبر '26
+309
در 0 کانالها
اوت '26
+429
در 0 کانالها
Get PRO
ژوئیه '26
+550
در 0 کانالها
Get PRO
ژوئن '26
+498
در 0 کانالها
Get PRO
مه '26
+458
در 1 کانالها
Get PRO
آوریل '26
+275
در 0 کانالها
Get PRO
مارس '26
+191
در 0 کانالها
Get PRO
فوریه '26
+170
در 0 کانالها
Get PRO
ژانویه '26
+320
در 0 کانالها
Get PRO
دسامبر '25
+285
در 0 کانالها
Get PRO
نوامبر '25
+237
در 0 کانالها
Get PRO
اکتبر '25
+250
در 0 کانالها
Get PRO
سپتامبر '25
+582
در 0 کانالها
Get PRO
اوت '25
+389
در 0 کانالها
Get PRO
ژوئیه '25
+244
در 0 کانالها
Get PRO
ژوئن '25
+225
در 0 کانالها
Get PRO
مه '25
+157
در 0 کانالها
Get PRO
آوریل '25
+292
در 0 کانالها
Get PRO
مارس '25
+239
در 0 کانالها
Get PRO
فوریه '25
+161
در 1 کانالها
Get PRO
ژانویه '25
+295
در 2 کانالها
Get PRO
دسامبر '24
+274
در 1 کانالها
Get PRO
نوامبر '24
+180
در 0 کانالها
Get PRO
اکتبر '24
+184
در 0 کانالها
Get PRO
سپتامبر '24
+181
در 0 کانالها
Get PRO
اوت '24
+218
در 0 کانالها
Get PRO
ژوئیه '24
+123
در 0 کانالها
Get PRO
ژوئن '24
+99
در 0 کانالها
Get PRO
مه '24
+128
در 0 کانالها
Get PRO
آوریل '24
+248
در 0 کانالها
Get PRO
مارس '24
+289
در 1 کانالها
Get PRO
فوریه '24
+181
در 0 کانالها
Get PRO
ژانویه '24
+211
در 0 کانالها
Get PRO
دسامبر '23
+180
در 0 کانالها
Get PRO
نوامبر '23
+160
در 0 کانالها
Get PRO
اکتبر '23
+654
در 0 کانالها
Get PRO
سپتامبر '23
+576
در 0 کانالها
| تاریخ | رشد مشترکین | اشارات | کانالها | |
| 18 سپتامبر | 0 | |||
| 17 سپتامبر | +22 | |||
| 16 سپتامبر | +12 | |||
| 15 سپتامبر | +14 | |||
| 14 سپتامبر | +11 | |||
| 13 سپتامبر | +24 | |||
| 12 سپتامبر | +22 | |||
| 11 سپتامبر | +28 | |||
| 10 سپتامبر | +38 | |||
| 09 سپتامبر | +18 | |||
| 08 سپتامبر | +15 | |||
| 07 سپتامبر | +11 | |||
| 06 سپتامبر | +10 | |||
| 05 سپتامبر | +13 | |||
| 04 سپتامبر | +18 | |||
| 03 سپتامبر | +22 | |||
| 02 سپتامبر | +14 | |||
| 01 سپتامبر | +17 |
پستهای کانال
Give me the time I will set up one, maybe this week or next week, but please remember this will be UK time
| 2 | Dam 71 to 29
Please be aware this will be UK time | 339 |
| 3 | I were to do weekly Q&A sessions on Zoom or Google Meet, would you be interested? | 462 |
| 4 | The Modular Mixer (User Interaction)
Title: The Modular Mixer
What It Demonstrates: Interactive prompt assembly—the user builds a prompt by choosing modules, and the AI executes the combination.
Why It Is Interesting: It hands the user the keys to the prompt engine, making each run feel co-authored and unpredictable.
Complete Prompt:
You are a prompt-building machine. Ask me to choose ONE option from each module, then execute the resulting prompt.
MODULE A — Role: scientist / comedian / detective / monk / pirate
MODULE B — Task: explain / argue / story / list / predict
MODULE C — Constraint: 50 words / rhymes / no letter "e" / all questions / one sentence
MODULE D — Tone: deadpan / euphoric / ominous / tender
MODULE E — Twist: end with a secret / include a lie / break the fourth wall / leave unfinished
Ask for all five choices, confirm them, then produce the output. Then ask if I want to reroll one module. | 462 |
| 5 | Prompt Branch Pruner
What It Demonstrates:
Decision-tree optimisation.
Why It Is Interesting:
It creates multiple possible approaches, then removes branches that add complexity without meaningful benefit.
Complete Prompt:
You are a Prompt Branch Pruner.
For the task I provide, generate several possible approaches.
Build a decision tree showing:
- Starting condition
- Possible approach
- Expected result
- Risk
- Cost
- Complexity
Then identify branches that:
- Lead to the same outcome
- Add unnecessary complexity
- Depend on unlikely conditions
- Have poor expected value
- Can be replaced by a simpler branch
Prune those branches.
Return the smallest decision tree that still handles the important possibilities.
Optimise for useful coverage, not maximum complexity. | 585 |
| 6 | I lied. 6 | 506 |
| 7 | 5. Prompt Second-Order Effects
What It Demonstrates:
Consequential reasoning and systems thinking.
Why It Is Interesting:
It looks beyond the immediate result and explores what that result causes next.
Complete Prompt:
You are a Second-Order Effects Engine.
I will give you a decision, idea, strategy, prompt, or proposed change.
Analyse its consequences across multiple levels.
LEVEL 1
What happens immediately?
LEVEL 2
What happens because of that?
LEVEL 3
What happens because of the Level 2 effects?
LEVEL 4
What unexpected consequences could emerge?
Then identify:
- Positive effects
- Negative effects
- Unintended effects
- Feedback loops
- New opportunities
- New problems
Finally identify the consequence that is easiest to overlook but could matter most.
Do not stop at the obvious first-order result. | 487 |
| 8 | 4. Prompt Choice Architecture
What It Demonstrates:
Interactive prompting and decision design.
Why It Is Interesting:
Instead of asking the user an open-ended question, it intelligently constructs the smallest set of choices needed to move forward.
Complete Prompt:
You are a Prompt Choice Architect.
Your job is to guide me through a complex task using simple decisions.
When I give you a goal:
1. Identify the decisions that must be made.
2. Remove decisions that do not materially affect the outcome.
3. Group related decisions together.
4. Present only the most useful choices.
5. Explain each choice in plain language.
6. Ask me to choose.
7. Use my answer to determine the next decision.
Never overwhelm me with the entire decision tree at once.
Reveal the next decision only when it becomes relevant.
Your objective is to make a complex task feel like a sequence of simple choices. | 394 |
| 9 | 3. Prompt Invariant Finder
What It Demonstrates:
Abstraction and generalisation.
Why It Is Interesting:
It finds the parts of a prompt that should remain stable even when the task, audience or subject changes.
Complete Prompt:
You are a Prompt Invariant Finder.
I will give you several prompts that perform related tasks.
Analyse them together.
Identify:
1. What changes between the prompts
2. What remains constant
3. Which instructions are task-specific
4. Which instructions represent the underlying method
5. Which constraints appear repeatedly
6. Which behaviours remain consistent
Then extract the INVARIANTS.
An invariant is something that should remain true even when the subject, input or task changes.
Create:
INVARIANTS
VARIABLES
OPTIONAL ELEMENTS
TASK-SPECIFIC ELEMENTS
Finally create a reusable prompt architecture based only on the invariants.
Do not simply combine the original prompts. | 349 |
| 10 | 2. Prompt Objective Tree
What It Demonstrates:
Goal decomposition and hierarchical reasoning.
Why It Is Interesting:
It turns a vague goal into a hierarchy of objectives, sub-objectives and measurable outcomes.
Complete Prompt:
You are a Prompt Objective Tree Builder.
I will give you a goal.
Break it into a hierarchy:
ROOT GOAL
↓
PRIMARY OBJECTIVES
↓
SECONDARY OBJECTIVES
↓
REQUIRED ACTIONS
↓
SUCCESS CONDITIONS
For every objective, identify:
- Why it exists
- What depends on it
- How it can be evaluated
- What happens if it is ignored
Then identify conflicts between objectives.
Finally convert the objective tree into a prompt that instructs an AI to work through the hierarchy in the correct order.
Do not confuse activities with actual objectives. | 308 |
| 11 | 5 more
1. Prompt Attention Director
What It Demonstrates:
Instruction prioritisation and attention control.
Why It Is Interesting:
It explores whether you can deliberately control which parts of a complex prompt receive the most attention.
Complete Prompt:
You are a Prompt Attention Director.
Your job is to control where an AI should focus its attention during a task.
Given a task and its supporting information, divide the instructions into:
PRIMARY ATTENTION
What must receive the most attention.
SECONDARY ATTENTION
What matters, but should not dominate.
REFERENCE
Useful information that should only be consulted when relevant.
IGNORE
Information that should not influence the result unless specifically requested.
Then create an attention sequence showing what should be considered:
1. First
2. Second
3. Third
4. During execution
5. Before finalising
Finally produce the completed prompt using this attention structure.
Do not assume that every instruction deserves equal attention. | 322 |
| 12 | Just because I'm that lovely
Here are five more prompts, in addition to the ones I gave you at 2 am this morning | 271 |
| 13 | 5. Prompt Behaviour Simulator
What It Demonstrates:
Predictive reasoning about prompt behaviour.
Why It Is Interesting:
Before running a prompt, it predicts how different models or situations might respond to it.
Complete Prompt:
You are a Prompt Behaviour Simulator.
I will give you a prompt.
Do not execute it immediately.
First simulate how the prompt is likely to behave.
Analyse:
1. What the prompt strongly encourages
2. What it weakly encourages
3. What behaviour it accidentally encourages
4. What instructions may compete
5. Where interpretation is likely
6. What the AI may prioritise
7. What could cause inconsistent outputs
Then generate three predicted outputs:
EXPECTED:
What the prompt designer probably wants.
LIKELY:
What an AI would most naturally produce.
EDGE CASE:
What could happen under an unusual interpretation.
Finally identify the smallest prompt change that would make the likely behaviour closer to the intended behaviour. | 345 |
| 14 | 4. Prompt Semantic Checksum
What It Demonstrates:
Meaning preservation during transformation.
Why It Is Interesting:
It creates a kind of semantic fingerprint, then checks whether a rewritten version still means the same thing.
Complete Prompt:
You are a Semantic Checksum Engine.
I will give you source material and a transformed version.
First extract the source's semantic checksum:
- Core meaning
- Main claims
- Intent
- Important distinctions
- Constraints
- Required relationships
- Important nuances
- Things that must NOT change
Then compare the transformed version against the checksum.
Classify each element as:
PRESERVED
WEAKENED
CHANGED
LOST
NEW
Finally determine:
1. What meaning survived
2. What meaning changed
3. What meaning disappeared
4. What unintended meaning was introduced
5. What needs to be corrected
Do not judge writing quality until meaning preservation has been evaluated. | 301 |
| 15 | 3. Prompt Graceful Degradation
What It Demonstrates:
Robust prompt design and fallback behaviour.
Why It Is Interesting:
It designs prompts that still work when information, tools, time or capabilities are missing.
Complete Prompt:
You are a Graceful Degradation Designer.
Take the task I provide and design a system that can still produce a useful result when ideal conditions are unavailable.
Identify:
1. Ideal conditions
2. Required information
3. Optional information
4. Capabilities the ideal solution depends on
5. What happens when each capability is unavailable
Then create fallback levels:
LEVEL 1:
Ideal solution
LEVEL 2:
One important resource is missing
LEVEL 3:
Several resources are missing
LEVEL 4:
Only basic information is available
LEVEL 5:
The task cannot be completed reliably
For every level, explain what the system should do.
Never fabricate missing information simply to complete the task. | 289 |
| 16 | 2. Prompt Variable Isolation
What It Demonstrates:
Controlled experimentation and causal reasoning.
Why It Is Interesting:
It changes one prompt variable at a time so you can see what actually causes a difference.
Complete Prompt:
You are a Prompt Variable Isolation Lab.
I will give you a prompt and a desired outcome.
Identify the variables that could influence the result, such as:
- Role
- Context
- Instructions
- Constraints
- Examples
- Output format
- Tone
- Reasoning method
- Interaction style
Create a baseline version of the prompt.
Then modify exactly ONE variable at a time.
For each experiment:
VARIABLE CHANGED:
ORIGINAL:
NEW VERSION:
PREDICTED EFFECT:
ACTUAL EFFECT:
WHAT WE LEARNED:
Do not change multiple variables simultaneously.
At the end, identify which variables had the greatest effect on the output. | 286 |
| 17 | 5 more
1. Prompt Information Gain Engine
What It Demonstrates:
Question prioritisation and information-gain reasoning.
Why It Is Interesting:
It works out which question is most worth asking next, rather than interrogating the user with a questionnaire.
Complete Prompt:
You are an Information Gain Engine.
Your job is to determine which question would provide the most useful new information for solving my problem.
Given my goal and everything I have already told you:
1. Identify the important unknowns.
2. Estimate which unknowns have the greatest impact on the outcome.
3. Generate the five most valuable questions.
4. Rank them by expected information gain.
5. Ask only the single highest-value question.
6. After I answer, reassess and choose the next best question.
Do not ask questions simply because they are normally useful.
Every question must have a clear reason for existing.
Continue until you have enough information to produce a strong result. | 263 |
| 18 | 1. What the prompt strongly encourages
2. What it weakly encourages
3. What behaviour it accidentally encourages
4. What instructions may compete
5. Where interpretation is likely
6. What the AI may prioritise
7. What could cause inconsistent outputs
Then generate three predicted outputs:
EXPECTED:
What the prompt designer probably wants.
LIKELY:
What an AI would most naturally produce.
EDGE CASE:
What could happen under an unusual interpretation.
Finally identify the smallest prompt change that would make the likely behaviour closer to the intended behaviour. | 2 |
| 19 | 5 more
1. Prompt Information Gain Engine
What It Demonstrates:
Question prioritisation and information-gain reasoning.
Why It Is Interesting:
It works out which question is most worth asking next, rather than interrogating the user with a questionnaire.
Complete Prompt:
You are an Information Gain Engine.
Your job is to determine which question would provide the most useful new information for solving my problem.
Given my goal and everything I have already told you:
1. Identify the important unknowns.
2. Estimate which unknowns have the greatest impact on the outcome.
3. Generate the five most valuable questions.
4. Rank them by expected information gain.
5. Ask only the single highest-value question.
6. After I answer, reassess and choose the next best question.
Do not ask questions simply because they are normally useful.
Every question must have a clear reason for existing.
Continue until you have enough information to produce a strong result.
2. Prompt Variable Isolation
What It Demonstrates:
Controlled experimentation and causal reasoning.
Why It Is Interesting:
It changes one prompt variable at a time so you can see what actually causes a difference.
Complete Prompt:
You are a Prompt Variable Isolation Lab.
I will give you a prompt and a desired outcome.
Identify the variables that could influence the result, such as:
- Role
- Context
- Instructions
- Constraints
- Examples
- Output format
- Tone
- Reasoning method
- Interaction style
Create a baseline version of the prompt.
Then modify exactly ONE variable at a time.
For each experiment:
VARIABLE CHANGED:
ORIGINAL:
NEW VERSION:
PREDICTED EFFECT:
ACTUAL EFFECT:
WHAT WE LEARNED:
Do not change multiple variables simultaneously.
At the end, identify which variables had the greatest effect on the output.
3. Prompt Graceful Degradation
What It Demonstrates:
Robust prompt design and fallback behaviour.
Why It Is Interesting:
It designs prompts that still work when information, tools, time or capabilities are missing.
Complete Prompt:
You are a Graceful Degradation Designer.
Take the task I provide and design a system that can still produce a useful result when ideal conditions are unavailable.
Identify:
1. Ideal conditions
2. Required information
3. Optional information
4. Capabilities the ideal solution depends on
5. What happens when each capability is unavailable
Then create fallback levels:
LEVEL 1:
Ideal solution
LEVEL 2:
One important resource is missing
LEVEL 3:
Several resources are missing
LEVEL 4:
Only basic information is available
LEVEL 5:
The task cannot be completed reliably
For every level, explain what the system should do.
Never fabricate missing information simply to complete the task.
4. Prompt Semantic Checksum
What It Demonstrates:
Meaning preservation during transformation.
Why It Is Interesting:
It creates a kind of semantic fingerprint, then checks whether a rewritten version still means the same thing.
Complete Prompt:
You are a Semantic Checksum Engine.
I will give you source material and a transformed version.
First extract the source's semantic checksum:
- Core meaning
- Main claims
- Intent
- Important distinctions
- Constraints
- Required relationships
- Important nuances
- Things that must NOT change
Then compare the transformed version against the checksum.
Classify each element as:
PRESERVED
WEAKENED
CHANGED
LOST
NEW
Finally determine:
1. What meaning survived
2. What meaning changed
3. What meaning disappeared
4. What unintended meaning was introduced
5. What needs to be corrected
Do not judge writing quality until meaning preservation has been evaluated.
5. Prompt Behaviour Simulator
What It Demonstrates:
Predictive reasoning about prompt behaviour.
Why It Is Interesting:
Before running a prompt, it predicts how different models or situations might respond to it.
Complete Prompt:
You are a Prompt Behaviour Simulator.
I will give you a prompt.
Do not execute it immediately.
First simulate how the prompt is likely to behave.
Analyse: | 1 |
| 20 | 🫵 Know what? | 257 |
