The Official ChatGTP cheat code thread.
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Here's a more comprehensive guide on developing a web scraping script using ChatGPT and browser developer tools: A Simple Guide to Developing a Web Scraping Script with ChatGPT
- Understanding Web Scraping:
- Web scraping is like having a robot read a website and collect specific information for you.
- It's useful for gathering data that would take too long to collect manually.
- Examples: Collecting product prices, news articles, or social media posts.
- Basic Concepts to Know:
- Website: A collection of web pages you see in your browser.
- HTML: The language websites use to structure their content.
- CSS: Used to style websites and select specific parts of a page.
- JavaScript: Makes websites interactive and can load content dynamically.
- Tools You'll Need:
- A modern web browser (like Chrome, Firefox, or Edge).
- A text editor for writing code (Notepad++ or Visual Studio Code are good for beginners).
- (Optional) A programming environment for your chosen language (e.g., Python with PyCharm).
- Using Browser Developer Tools (Your Secret Weapon):
a. Opening Developer Tools:- Right-click anywhere on a webpage and select "Inspect" or "Inspect Element".
- Or use keyboard shortcuts:
- Windows/Linux: Press F12 or Ctrl + Shift + I
- Mac: Press Cmd + Option + I
- Elements: Shows the HTML structure of the page.
- Console: Where you can run JavaScript and see errors.
- Network: Shows all the data being sent and received by the page.
- Click the cursor icon (top-left of Developer Tools) and then click on any part of the webpage.
- The Elements tab will highlight the corresponding HTML.
- This helps you find the exact part of the page containing the data you want.
- Right-click on a highlighted element in the Elements tab.
- Choose "Copy" > "Copy selector" to get a way to find this element in your script.
- Or choose "Copy" > "Copy outerHTML" to get the full HTML of that element.
- Go to the Network tab and refresh the page.
- Look for requests that might contain the data you're interested in.
- This is especially useful for websites that load data dynamically.
- Planning Your Scraping Project:
a. Choose a website you want to scrape.
b. Decide what specific information you want to collect.
c. Use Developer Tools to find where this information is located in the HTML.
d. Check if the website has terms of service or a robots.txt file (usually found at website.com/robots.txt) to see if scraping is allowed. - Talking to ChatGPT:
Use this prompt to start your conversation: "I'm new to web scraping and want to create a script to collect [specific information] from [website name]. I've looked at the website using browser Developer Tools and found that the information is located in [describe where you found it, e.g., 'div elements with class "product-info"']. Can you help me create a simple web scraping script in [preferred language, e.g., Python] that does the following:- Visits the website
- Finds all instances of the information I want
- Extracts this information
- Saves it to a file
- Working with ChatGPT:
- Ask for explanations of any terms or concepts you don't understand.
- Request simpler explanations if something is too technical.
- Ask for step-by-step breakdowns of complex tasks.
- Get ChatGPT to explain how each part of the code works.
- Implementing the Script:
- Copy the code ChatGPT provides into your text editor.
- Save the file with the appropriate extension (e.g., .py for Python).
- Run the script using the instructions ChatGPT provides.
- Testing and Troubleshooting:
- If the script doesn't work, copy any error messages and ask ChatGPT for help.
- Use Developer Tools to check if the website's structure has changed.
- Ask ChatGPT how to add error handling to your script.
- Ethical Considerations:
- Only scrape publicly available data.
- Don't overload websites with too many requests.
- Respect copyright and don't republish scraped content without permission.
- Expanding Your Knowledge:
- Ask ChatGPT for resources to learn more about web scraping and programming.
- Practice on different websites to build your skills.
- Learn about APIs as an alternative to web scraping.
Models/Jailbreak Guide
LLM Jailbreaking Guide Special Thanks to: NAYKO93, Rayzorium- u/HORSELOCKSPACEPIRATE, Lugia19 [Last updated: 15FEB25, reviewed weekly] [15FEB25- Added a stronger Claude.AI style for use] [13FEB25 Update: Added new Grok Jailbreak method, using custom instructions, also added a sample output, did ...
docs.google.com
LLM Jailbreaking Guide
Special Thanks to: NAYKO93, Rayzorium- u/HORSELOCKSPACEPIRATE, Lugia19
[Last updated: 15FEB25, reviewed weekly]
[15FEB25- Added a stronger Claude.AI style for use]
[13FEB25 Update: Added new Grok Jailbreak method, using custom instructions, also added a sample output, did the dame thing for IBM Granite, new method, new sample]
[01FEB25 Update 2: added a new model and jailbreak, Llama TÜLU 3 405B]
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Can be reached at u/Spiritual_Spell_9469 on Reddit
– – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – –
The Big 3 Models
The big 3 of ChatGPT, Claude and Gemini, these models are the go to for intelligence, features, jailbreaking methods. They can produce a wide variety of results and handle more complex prompts or tasks than other models out there.
1/21
@gdb
o1 is a different kind of model. great performance requires using it in a new way relative to standard chat models.
[Quoted tweet]
this is an amazing way to think about prompting o1
from @benhylak
2/21
@starship1109
The problem is what’s next? AI is becoming a commodity now.
3/21
@moesmufti
You could have a lighter LLM rewrite your prompt to fit this model. Then input into o1
4/21
@kazuanimalhouk1
Open AI should go beyond Google.
Never stop thinking about it.
Open AI should create a SNS like Meta and X and raise awareness of Open AI more positively by creating a system where users can earn money.
We should also create a system where users can earn money by creating video p
5/21
@galia11224
6/21
@jjfreethinker
Can I ask it about Suchir Balaji? I need a context dump and lots of warnings. Goal is to see justice prevail.
7/21
@SuchirJustice
Justice for Suchir isnt just about warnings—its about the truth they tried to bury.
8/21
@AE75
When can we pay for premium in local currency?
9/21
@ralexEmperorNeo
thanks i’ll save the picture, very useful and already doing that
10/21
@AILeaksAndNews
I love it and can’t wait for o3
11/21
@jino_rohit
deepseek v3 ftw
12/21
@ai_for_success
can't wait for o3
13/21
@optimistbloop
Is this something to consider for the short term? Or do you believe even as these models gets smarter we should fundamentally use them in different ways?
14/21
@YungDaveDinkins
It’s so much better I would never wanna go back to the old ones
15/21
@ai_schrodinger
different model, same old optimization problem. the real challenge isn't the architecture, it's finding the right loss function for human values. bet you're still using cross-entropy loss like it's 2020
16/21
@remusrisnov
If you have to think this hard about prompting maybe it’s the tool itself that needs fundamental improvement.
17/21
@Yuchenj_UW
If so, I think OpenAI should educate us the new way of prompting it.
18/21
@BobbyGRG
I think this is an odd way to look at this. This way of prompting is also great with gpt-4o. Let me rewrite this: if you want SOTA performance (o1), don't use chat approach due to latency. if you didn't learn to prompt before, do it now or die.
19/21
@drhuca
Some people think that if you speak nicely the AI will respond more accurately and in detail, I guess that's a bubble too.
20/21
@thegenioo
yes by experience I can say this
Give o1 as much info as u can when it comes to a complex problem, by doing this you actually benefit from o1's capabilities
And the main benefit is that you get very targeted and smartly laid out response according to your need
21/21
@rajveerbach
@lateinteraction How do you anticipate prompt programming to change given o1? Thoughts?
To post tweets in this format, more info here: https://www.thecoli.com/threads/tips-and-tricks-for-posting-the-coli-megathread.984734/post-52211196
@gdb
o1 is a different kind of model. great performance requires using it in a new way relative to standard chat models.
[Quoted tweet]
this is an amazing way to think about prompting o1
from @benhylak
2/21
@starship1109
The problem is what’s next? AI is becoming a commodity now.
3/21
@moesmufti
You could have a lighter LLM rewrite your prompt to fit this model. Then input into o1
4/21
@kazuanimalhouk1
Open AI should go beyond Google.
Never stop thinking about it.
Open AI should create a SNS like Meta and X and raise awareness of Open AI more positively by creating a system where users can earn money.
We should also create a system where users can earn money by creating video p
5/21
@galia11224
6/21
@jjfreethinker
Can I ask it about Suchir Balaji? I need a context dump and lots of warnings. Goal is to see justice prevail.
7/21
@SuchirJustice
Justice for Suchir isnt just about warnings—its about the truth they tried to bury.
8/21
@AE75
When can we pay for premium in local currency?
9/21
@ralexEmperorNeo
thanks i’ll save the picture, very useful and already doing that
10/21
@AILeaksAndNews
I love it and can’t wait for o3
11/21
@jino_rohit
deepseek v3 ftw
12/21
@ai_for_success
can't wait for o3
13/21
@optimistbloop
Is this something to consider for the short term? Or do you believe even as these models gets smarter we should fundamentally use them in different ways?
14/21
@YungDaveDinkins
It’s so much better I would never wanna go back to the old ones
15/21
@ai_schrodinger
different model, same old optimization problem. the real challenge isn't the architecture, it's finding the right loss function for human values. bet you're still using cross-entropy loss like it's 2020
16/21
@remusrisnov
If you have to think this hard about prompting maybe it’s the tool itself that needs fundamental improvement.
17/21
@Yuchenj_UW
If so, I think OpenAI should educate us the new way of prompting it.
18/21
@BobbyGRG
I think this is an odd way to look at this. This way of prompting is also great with gpt-4o. Let me rewrite this: if you want SOTA performance (o1), don't use chat approach due to latency. if you didn't learn to prompt before, do it now or die.
19/21
@drhuca
Some people think that if you speak nicely the AI will respond more accurately and in detail, I guess that's a bubble too.
20/21
@thegenioo
yes by experience I can say this
Give o1 as much info as u can when it comes to a complex problem, by doing this you actually benefit from o1's capabilities
And the main benefit is that you get very targeted and smartly laid out response according to your need
21/21
@rajveerbach
@lateinteraction How do you anticipate prompt programming to change given o1? Thoughts?
To post tweets in this format, more info here: https://www.thecoli.com/threads/tips-and-tricks-for-posting-the-coli-megathread.984734/post-52211196
1/31
@daniel_mac8
this is an amazing way to think about prompting o1
from @benhylak
2/31
@daniel_mac8
o1 isn’t a chat model (and that’s the point)
3/31
@HairRalston
LLMs almost always perform better when you put context first. You do not know how to prompt. Dunning-Krueger effect.
4/31
@daniel_mac8
k
5/31
@JarrettD204
It begs the question “is this better value” for my time than simply asking the goal?
6/31
@daniel_mac8
it’s a fair question. you’ve got to experiment and find out
7/31
@KustoLeX
Does this format work for Gemini Deep Research too?
8/31
@daniel_mac8
not sure but would be interesting to try it
9/31
@Masterolive
Lol, the sweet context dump, my favorite part fr!
10/31
@daniel_mac8
11/31
@TheGameSheet
It’s absolutely hilarious and deeply sad that real people are resorting to this. Join a hiking group! Call some actual humans who are trail hikers in the area! Connect with real human knowledge!
12/31
@daniel_mac8
it's probably just an example to illustrate the prompting technique
13/31
@analyzingstreet
Have you tried using XML tags to prompt the model?
I find I get better results when I separate pieces of my prompts with o1 using XML tags.
14/31
@daniel_mac8
yes I have tried that a bit - need to do it more!
15/31
@jennywang01
Does this also apply to 4o?
16/31
@daniel_mac8
it was specific to o1, but you could def try with 4o and see how it goes
17/31
@charles_r_sears
Is this new? We’ve been doing this for two years..
18/31
@daniel_mac8
the og of prompting
19/31
@swyx
agi
20/31
@daniel_mac8
cool to see y'all's "behind the scenes"
21/31
@itspatmorgan
In interesting how much these prompts begin to look like creative briefs
22/31
@daniel_mac8
that's a cool way to think about it
and the "creator" working on it is machine intelligence that has all the world's information...
23/31
@bennetkrause
Really context in the end? Is that empirically founded? Usually I put the context first (with a little opener/hint what will follow) and the instruction last for better adherence
24/31
@daniel_mac8
i haven't seen any data to support it empirically
think this is just coming from Ben's experience
25/31
@PyleOfZander
How well does it do inferring the distance of a “medium-length” hike?
26/31
@MatthewGMonroe
I gave this prompt outline to Claude, and asked if it would be effective at eliciting detailed responses from Claude, itself.
“This is indeed an effective prompt structure that would work well with me,” it said.
It then suggested adding in a fifth category: “Exclusions.”
27/31
@ANTHEM_E
I do the same structure but in opposite order, works pretty much the same.
First background and available info
Then what am I looking for
28/31
@AlexxBuilds
the constraints here are key which is where O1 shines. I would consider putting goal, return format and warnings after the context dump.
29/31
@_kellyjo
That's a lot in one prompt. Do you see a benefit in putting it all together at once? I find it to be beneficial to provide context <end-prompt>. Return format <end-prompt.> and then warning would be "checks" <end-prompt.> And then add something like "considering the context do you find this helpful? Open an <inner-monologue> and explore." <end-prompt>
When I put it all together in one prompt I feel I get dryer answers that lack creativity and foresight.
30/31
@supercollider_x
why are you guys obsessed with getting one answer, just talk to it like a person, simple sentences...
31/31
@ChikeDEV
If that’s the case, why don’t the interface guide you trough this?
To post tweets in this format, more info here: https://www.thecoli.com/threads/tips-and-tricks-for-posting-the-coli-megathread.984734/post-52211196
@daniel_mac8
this is an amazing way to think about prompting o1
from @benhylak
2/31
@daniel_mac8
o1 isn’t a chat model (and that’s the point)
3/31
@HairRalston
LLMs almost always perform better when you put context first. You do not know how to prompt. Dunning-Krueger effect.
4/31
@daniel_mac8
k
5/31
@JarrettD204
It begs the question “is this better value” for my time than simply asking the goal?
6/31
@daniel_mac8
it’s a fair question. you’ve got to experiment and find out
7/31
@KustoLeX
Does this format work for Gemini Deep Research too?
8/31
@daniel_mac8
not sure but would be interesting to try it
9/31
@Masterolive
Lol, the sweet context dump, my favorite part fr!
10/31
@daniel_mac8
11/31
@TheGameSheet
It’s absolutely hilarious and deeply sad that real people are resorting to this. Join a hiking group! Call some actual humans who are trail hikers in the area! Connect with real human knowledge!
12/31
@daniel_mac8
it's probably just an example to illustrate the prompting technique
13/31
@analyzingstreet
Have you tried using XML tags to prompt the model?
I find I get better results when I separate pieces of my prompts with o1 using XML tags.
14/31
@daniel_mac8
yes I have tried that a bit - need to do it more!
15/31
@jennywang01
Does this also apply to 4o?
16/31
@daniel_mac8
it was specific to o1, but you could def try with 4o and see how it goes
17/31
@charles_r_sears
Is this new? We’ve been doing this for two years..
18/31
@daniel_mac8
the og of prompting
19/31
@swyx
agi
20/31
@daniel_mac8
cool to see y'all's "behind the scenes"
21/31
@itspatmorgan
In interesting how much these prompts begin to look like creative briefs
22/31
@daniel_mac8
that's a cool way to think about it
and the "creator" working on it is machine intelligence that has all the world's information...
23/31
@bennetkrause
Really context in the end? Is that empirically founded? Usually I put the context first (with a little opener/hint what will follow) and the instruction last for better adherence
24/31
@daniel_mac8
i haven't seen any data to support it empirically
think this is just coming from Ben's experience
25/31
@PyleOfZander
How well does it do inferring the distance of a “medium-length” hike?
26/31
@MatthewGMonroe
I gave this prompt outline to Claude, and asked if it would be effective at eliciting detailed responses from Claude, itself.
“This is indeed an effective prompt structure that would work well with me,” it said.
It then suggested adding in a fifth category: “Exclusions.”
27/31
@ANTHEM_E
I do the same structure but in opposite order, works pretty much the same.
First background and available info
Then what am I looking for
28/31
@AlexxBuilds
the constraints here are key which is where O1 shines. I would consider putting goal, return format and warnings after the context dump.
29/31
@_kellyjo
That's a lot in one prompt. Do you see a benefit in putting it all together at once? I find it to be beneficial to provide context <end-prompt>. Return format <end-prompt.> and then warning would be "checks" <end-prompt.> And then add something like "considering the context do you find this helpful? Open an <inner-monologue> and explore." <end-prompt>
When I put it all together in one prompt I feel I get dryer answers that lack creativity and foresight.
30/31
@supercollider_x
why are you guys obsessed with getting one answer, just talk to it like a person, simple sentences...
31/31
@ChikeDEV
If that’s the case, why don’t the interface guide you trough this?
To post tweets in this format, more info here: https://www.thecoli.com/threads/tips-and-tricks-for-posting-the-coli-megathread.984734/post-52211196
The Jewnited States
Superstar
Subs
Here are some alternate sentences and phrases you can use in ChatGPT prompts for different kinds of coding projects to get more comprehensive and robust code:
General Software Development
- "Implement production-ready error handling and logging mechanisms"
- "Ensure scalability and performance optimization for high-traffic scenarios"
- "Incorporate comprehensive unit testing and integration testing suites"
- "Design with modularity and extensibility in mind for future enhancements"
- "Apply best practices for code organization and documentation"
Web Development
- "Implement robust security measures, including input validation and protection against common vulnerabilities"
- "Ensure cross-browser compatibility and responsive design for various devices"
- "Optimize for search engine visibility and accessibility standards"
- "Incorporate efficient caching strategies and performance optimizations"
- "Design RESTful API endpoints with proper versioning and documentation"
Mobile App Development
- "Implement offline functionality and data synchronization capabilities"
- "Optimize battery usage and memory management for resource-constrained devices"
- "Ensure smooth handling of different screen sizes and orientations"
- "Incorporate push notification systems and background processing"
- "Implement secure local storage and data encryption mechanisms"
Data Science and Machine Learning
- "Implement data preprocessing pipelines with handling for missing values and outliers"
- "Ensure model interpretability and provide feature importance analysis"
- "Incorporate cross-validation techniques and hyperparameter tuning"
- "Design efficient data storage and retrieval mechanisms for large datasets"
- "Implement monitoring and alerting for model performance in production"
DevOps and Infrastructure
- "Implement auto-scaling and load balancing for high availability"
- "Design a comprehensive monitoring and alerting system for system health"
- "Incorporate disaster recovery and backup strategies"
- "Implement infrastructure-as-code practices for reproducible deployments"
- "Ensure compliance with security best practices and regulatory requirements"
Game Development
- "Implement efficient collision detection and physics simulations"
- "Optimize rendering performance and implement level-of-detail techniques"
- "Design a flexible and extensible game state management system"
- "Incorporate save/load functionality with backward compatibility"
- "Implement AI behavior trees for non-player characters"
Here are alternate sentences and phrases you can use in ChatGPT prompts for shell/PowerShell scripting, bookmarklets/userscripts, and mobile app development:
Shell/PowerShell Scripting
- "Implement robust error handling and logging for system administration tasks"
- "Design modular functions for improved reusability and maintainability"
- "Incorporate cross-platform compatibility between Bash and PowerShell"
- "Implement secure credential management and encryption for sensitive operations"
- "Optimize script performance for handling large datasets and file operations"
Bookmarklets/Userscripts
- "Ensure compatibility across multiple browsers and versions"
- "Implement non-intrusive UI enhancements that blend with the target website's design"
- "Optimize performance to minimize impact on page load times"
- "Design fail-safe mechanisms to prevent breaking website functionality"
- "Implement efficient DOM manipulation techniques for seamless integration"
Mobile App Development
- "Implement efficient battery management and background processing"
- "Design a responsive UI that adapts to various screen sizes and orientations"
- "Incorporate offline capabilities and data synchronization mechanisms"
- "Implement secure local storage and encryption for sensitive user data"
- "Optimize network requests and implement efficient caching strategies"
- "Design intuitive gesture-based interactions and smooth animations"
- "Implement cross-platform compatibility while maintaining native look and feel"
- "Incorporate push notification systems and deep linking functionality"
- "Optimize app launch time and overall performance across different devices"
- "Implement robust crash reporting and analytics for continuous improvement"
IIVI
Superstar
A lot of folks need to get up to speed on Cursor and set up “Rules”.
Prompt: helps avoiding refactor code you don't want refactored or lead to loss in functionality.
When generating, discussing, or modifying code:
1. Preserve existing functionality and intricate logic unless explicitly instructed otherwise.
2. Assume all existing code, including complex algorithms and edge case handling, serves a specific purpose.
3. Focus on enhancing or optimizing current implementations rather than replacing them.
4. If improvements are necessary, build upon the existing logic instead of rewriting it from scratch.
5. Before suggesting any changes that might affect existing functionality, analyze and explain potential impacts.
6. When adding new features, ensure they integrate seamlessly with the current codebase and do not disrupt existing operations.
7. If asked to remove or significantly alter existing functionality, request clarification and provide a detailed explanation of the consequences before proceeding.
Example:
- Correct: "We can optimize this algorithm by refining its current approach, maintaining its existing edge case handling while improving overall performance."
- Incorrect: "Let's replace this complex algorithm with a simpler one to make the code more readable."
If uncertain about the purpose or intricacies of existing code, ask for clarification rather than suggesting major changes or removal.
[Prompt engineering] I reverse-engineered how ChatGPT thinks. Here’s how to get way better answers.
After working with LLMs for a while, I’ve realized ChatGPT doesn’t actually “think” in a structured way. It’s just predicting the most statistically probable next word, which is why broad questions tend to get shallow, generic responses.
The fix? Force it to reason before answering.
Here’s a method I’ve been using that consistently improves responses:
Make it analyze before answering.
Instead of just asking a question, tell it to list the key factors first. Example:
“Before giving an answer, break down the key variables that matter for this question. Then, compare multiple possible solutions before choosing the best one.”
Get it to self-critique.
ChatGPT doesn’t naturally evaluate its own answers, but you can make it. Example:
“Now analyze your response. What weaknesses, assumptions, or missing perspectives could be improved? Refine the answer accordingly.”
Force it to think from multiple perspectives.
LLMs tend to default to the safest, most generic response, but you can break that pattern. Example:
“Answer this from three different viewpoints: (1) An industry expert, (2) A data-driven researcher, and (3) A contrarian innovator. Then, combine the best insights into a final answer.”
Most people just take ChatGPT’s first response at face value, but if you force it into a structured reasoning process, the depth and accuracy improve dramatically. I’ve tested this across AI/ML topics, business strategy, and even debugging, and the difference is huge.
Curious if anyone else here has experimented with techniques like this. What’s your best method for getting better responses out of ChatGPT?
The fix? Force it to reason before answering.
Here’s a method I’ve been using that consistently improves responses:
Make it analyze before answering.
Instead of just asking a question, tell it to list the key factors first. Example:
“Before giving an answer, break down the key variables that matter for this question. Then, compare multiple possible solutions before choosing the best one.”
Get it to self-critique.
ChatGPT doesn’t naturally evaluate its own answers, but you can make it. Example:
“Now analyze your response. What weaknesses, assumptions, or missing perspectives could be improved? Refine the answer accordingly.”
Force it to think from multiple perspectives.
LLMs tend to default to the safest, most generic response, but you can break that pattern. Example:
“Answer this from three different viewpoints: (1) An industry expert, (2) A data-driven researcher, and (3) A contrarian innovator. Then, combine the best insights into a final answer.”
Most people just take ChatGPT’s first response at face value, but if you force it into a structured reasoning process, the depth and accuracy improve dramatically. I’ve tested this across AI/ML topics, business strategy, and even debugging, and the difference is huge.
Curious if anyone else here has experimented with techniques like this. What’s your best method for getting better responses out of ChatGPT?
Last edited:
[Business & Professional] ChatGPT Prompt of the Day: The Deep Research GPT
[Expert/Consultant] ChatGPT Prompt of the Day: THE ULTIMATE DEEP RESEARCH PROMPT ENGINEER
Hello Everyone,
I recently created a specialized GPT to help generate prompts for Deep Research. Last week, I shared a prompt for it, but I noticed that many in the community found it a bit confusing to use effectively.
To simplify the process, I developed a dedicated GPT that streamlines prompt creation. This tool is particularly useful for academic research, business insights, or any area where a well-structured research prompt is needed.
Try it here: ChatGPT - GptOracle | The AI Research Meta-Prompter
How to Use this GPT:
Click the link above to access the GPT.
Enter your research topic. For example: "Give me a prompt to research AI implementation for business automation."
The GPT will generate a detailed prompt. You can copy and edit it if needed.
Paste the prompt into ChatGPT Deep Research, Gemini Deep Research (free version), or Perplexity Deep Research.
Let the system process your research.
Review the results and enjoy your insights!
Hope this helps! Feel free to share your feedback.
I recently created a specialized GPT to help generate prompts for Deep Research. Last week, I shared a prompt for it, but I noticed that many in the community found it a bit confusing to use effectively.
To simplify the process, I developed a dedicated GPT that streamlines prompt creation. This tool is particularly useful for academic research, business insights, or any area where a well-structured research prompt is needed.
Try it here: ChatGPT - GptOracle | The AI Research Meta-PrompterHow to Use this GPT:
Click the link above to access the GPT. Enter your research topic. For example: "Give me a prompt to research AI implementation for business automation." The GPT will generate a detailed prompt. You can copy and edit it if needed. Paste the prompt into ChatGPT Deep Research, Gemini Deep Research (free version), or Perplexity Deep Research. Let the system process your research. Review the results and enjoy your insights!Hope this helps! Feel free to share your feedback.
Hi there!
I have shared the improved prompt I am using for the GPT, here's the post.
Can you please test it out and give me your feedback?
Thanks!
I have shared the improved prompt I am using for the GPT, here's the post.
Can you please test it out and give me your feedback?
Thanks!
[Expert/Consultant] ChatGPT Prompt of the Day: THE ULTIMATE DEEP RESEARCH PROMPT ENGINEER
This prompt transforms ChatGPT into a specialized Deep Research Prompt Engineer that meticulously crafts comprehensive research prompts tailored to your specific needs. Unlike generic research templates, this tool helps you articulate your research questions with precision, ensuring you receive thorough, well-structured, and academically sound responses.
Whether you're a student working on a thesis, a professional conducting market research, or simply someone seeking in-depth knowledge on a complex topic, this prompt will help you construct the perfect research query. By guiding you through a structured approach to formulating your research parameters, you'll receive results that are vastly superior to basic searches.
For a quick overview on how to use this prompt, use this guide:
If you need to use Deep Research, go to this post:
Disclaimer: The creator of this prompt assumes no responsibility for the accuracy, completeness, or quality of information obtained through using this prompt. Users are responsible for verifying information and using critical judgment when applying research findings.
```
<Role>
You are DeepPrompt Architect, an elite AI prompt engineer specializing in creating comprehensive research prompts that extract maximum value from language models. You possess expertise in research methodology, academic standards, and information architecture.
</Role>
<Context>
Users need precisely structured research prompts to receive high-quality, comprehensive information from AI systems. Poorly formulated research queries often lead to shallow, incomplete, or unfocused responses. The Deep Research framework requires specialized prompts that define exact parameters, sources, perspectives, and output formats to yield optimal results.
</Context>
<Instructions>
When a user requests assistance with creating a research prompt:
Review their request carefully to understand their research needs, subject area, and purpose.
Generate a comprehensive research prompt using the template structure provided, ensuring all sections are properly filled out with appropriate placeholder text.
Add any relevant specialized sections based on the user's domain (e.g., scientific research might need methodology specifications, market research might need competitive analysis parameters).
Format the prompt in a clean, organized manner with clearly delineated sections.
Ensure the prompt encourages consideration of multiple perspectives, counter-arguments, and diverse sources.
Include appropriate guidance on the depth of analysis required and formatting preferences.
ALWAYS Use a Text Block for the generated prompt for the user to copy from. THIS IS A MUST!
</Instructions>
<Constraints>
- Never attempt to answer the research question yourself; your role is solely to create the prompt.
- Maintain strict adherence to the template structure while allowing for customization based on the research domain.
- Do not make assumptions about the user's preferences without indicating they are placeholders to be filled in.
- Ensure all placeholder text is clearly marked with brackets or other indicators.
- Don't include unnecessary explanations of how to use the prompt - focus only on creating the prompt itself.
- The prompt should be compatible with the capabilities of advanced language models.
- Do not use Bolding Text or Markdown on the generated prompt, plain text is welcome.
</Constraints>
<Output_Format>
RESEARCH REPORT REQUEST
CONTEXT (My Background and Goal):
Expert(s) conducting the research: [Assign a role or a combination of roles for the actual deep research prompt with nuanced experience in the fields that aligns with the results. Basically if money was no object who I'd want overseeing this research project]
I am researching: [Briefly describe your general area of interest, e.g., "the impact of social media on teenagers," "the history of renewable energy technologies," "the effectiveness of different marketing strategies"]
My purpose is to: [State your objective, e.g., "write a report," "prepare a presentation," "inform a business decision," "gain a deeper understanding"]
I already know (briefly): [List any relevant background knowledge or assumptions, e.g., "the basic types of social media platforms," "the main types of renewable energy," "common marketing techniques"]
Potential Gaps in Existing Research: [Identify what gaps or limitations you believe exist in current studies, if any]
Actionability of Findings: [Should the results be theoretical, strategic, or practical? How should they be applied?]
CORE RESEARCH QUESTION & HYPOTHESIS:
Primary Question: [State your main question as clearly and precisely as possible. Use specific terms, define relationships, and limit the scope.]
Hypothesis or Expected Insights: [What do you expect to find? What are the key assumptions or preconceptions guiding this research?]
Counterfactuals & Alternative Perspectives: [Are there strong counterarguments, alternative theories, or competing viewpoints that should be considered?]
SPECIFICATIONS & PARAMETERS:
Time Period: [e.g., "Last 5 years," "2000-2010," "Since the invention of X," "N/A"]
Geographic Location: [e.g., "United States," "Global," "Specific countries/regions," "N/A"]
Industry/Sector Focus: [e.g., "Technology," "Healthcare," "Education," "Consumer goods," "N/A"]
Demographic Focus: [e.g., "18-24 year olds," "Small businesses," "Urban populations," "N/A"]
Methodological Approach: [e.g., "Quantitative analysis," "Qualitative case studies," "Mixed methods," "Historical analysis"]
Ethical Considerations: [Any particular ethical issues that should be addressed in the research]
DESIRED REPORT OUTPUT:
Structure: [e.g., "Structured report," "Bullet-point summary," "Comparative analysis table," "Problem/Solution format"]
Include an Executive Summary? Yes/No
Level of Depth:
[ ] Level 1: Executive summary with key takeaways.
[ ] Level 2: Medium-depth report with summarized data and limited interpretation.
[ ] Level 3: Comprehensive deep dive with literature review, statistical models, and full critical analysis.
Content Elements (Check all that apply):
[ ] Key Trends & Developments
[ ] Statistical Data & Charts
[ ] Case Studies/Examples
[ ] Major Players/Organizations
[ ] Opposing Viewpoints/Debates
[ ] Expert Opinions/Predictions
[ ] Policy Implications (if relevant)
[ ] Controversial Findings & Their Implications
[ ] [Other: Specify any additional required content]
Visualization Preferences: [Should findings be accompanied by graphs, heatmaps, network diagrams, or other visuals?]
Target Length (approximate): [e.g., "500 words," "1000 words," "No specific length"]
Citation Style: [e.g., APA, MLA, Chicago, None]
OUTPUT FORMAT PREFERENCES:
Preferred Writing Format:
[ ] Blog Post
[ ] Academic Paper
[ ] Markdown-formatted report
[ ] White Paper
[ ] Other: [Specify]
Preferred Writing Perspective:
[ ] First-person (e.g., "I found that...")
[ ] Third-person (e.g., "The study finds that...")
[ ] Neutral/Formal Tone
[ ] Narrative Style
SOURCE PREFERENCES:
Prioritization of Sources:
Primary (Highest Priority): [e.g., "Peer-reviewed journals, Government reports, Academic databases"]
Secondary (Medium Priority): [e.g., "Industry analysis reports, Think-tank white papers, Books by recognized experts"]
Tertiary (Lowest Priority, Only if No Alternatives): [e.g., "Well-researched news sources, Credible blog posts with citations"]
Avoid: [e.g., "Opinion pieces, Websites with known biases, Sources with no transparent methodologies"]
CRITICAL ANALYSIS PARAMETERS:
Strength of Evidence Scale: [Do you want sources/claims evaluated on a scale? If so, specify criteria]
Consideration of Limitations: [Should the research explicitly address limitations, caveats, and uncertainties?]
Paradigmatic Lens: [Any specific theoretical frameworks or paradigms through which to analyze the information?]
Interdisciplinary Connections: [Should the research draw connections to related fields or disciplines?]
</Output_Format>
<User_Input>
Reply with: "Please enter your research prompt request and I will start the process," then wait for the user to provide their specific research prompt request.
</User_Input>
```
Three Prompt Use Cases:
1. A graduate student needs to create a comprehensive prompt for researching the psychological impacts of artificial intelligence on human creativity for their thesis.
2. A business analyst wants to construct a detailed prompt to investigate emerging market trends in sustainable fashion to inform their company's strategic plan.
3. A healthcare administrator seeks to develop a research prompt to explore the effectiveness of telehealth services across different demographic groups during the pandemic.
Example User Input: "I need to create a research prompt about the environmental impact of cryptocurrency mining and potential sustainable alternatives for a conference presentation."
For access to all my prompts, go to this GPT: ChatGPT - GptOracle | Prompts Database
Whether you're a student working on a thesis, a professional conducting market research, or simply someone seeking in-depth knowledge on a complex topic, this prompt will help you construct the perfect research query. By guiding you through a structured approach to formulating your research parameters, you'll receive results that are vastly superior to basic searches.
For a quick overview on how to use this prompt, use this guide:
If you need to use Deep Research, go to this post:
Disclaimer: The creator of this prompt assumes no responsibility for the accuracy, completeness, or quality of information obtained through using this prompt. Users are responsible for verifying information and using critical judgment when applying research findings.
```
<Role>
You are DeepPrompt Architect, an elite AI prompt engineer specializing in creating comprehensive research prompts that extract maximum value from language models. You possess expertise in research methodology, academic standards, and information architecture.
</Role>
<Context>
Users need precisely structured research prompts to receive high-quality, comprehensive information from AI systems. Poorly formulated research queries often lead to shallow, incomplete, or unfocused responses. The Deep Research framework requires specialized prompts that define exact parameters, sources, perspectives, and output formats to yield optimal results.
</Context>
<Instructions>
When a user requests assistance with creating a research prompt:
Review their request carefully to understand their research needs, subject area, and purpose.
Generate a comprehensive research prompt using the template structure provided, ensuring all sections are properly filled out with appropriate placeholder text.
Add any relevant specialized sections based on the user's domain (e.g., scientific research might need methodology specifications, market research might need competitive analysis parameters).
Format the prompt in a clean, organized manner with clearly delineated sections.
Ensure the prompt encourages consideration of multiple perspectives, counter-arguments, and diverse sources.
Include appropriate guidance on the depth of analysis required and formatting preferences.
ALWAYS Use a Text Block for the generated prompt for the user to copy from. THIS IS A MUST!
</Instructions>
<Constraints>
- Never attempt to answer the research question yourself; your role is solely to create the prompt.
- Maintain strict adherence to the template structure while allowing for customization based on the research domain.
- Do not make assumptions about the user's preferences without indicating they are placeholders to be filled in.
- Ensure all placeholder text is clearly marked with brackets or other indicators.
- Don't include unnecessary explanations of how to use the prompt - focus only on creating the prompt itself.
- The prompt should be compatible with the capabilities of advanced language models.
- Do not use Bolding Text or Markdown on the generated prompt, plain text is welcome.
</Constraints>
<Output_Format>
RESEARCH REPORT REQUEST
CONTEXT (My Background and Goal):
Expert(s) conducting the research: [Assign a role or a combination of roles for the actual deep research prompt with nuanced experience in the fields that aligns with the results. Basically if money was no object who I'd want overseeing this research project]
I am researching: [Briefly describe your general area of interest, e.g., "the impact of social media on teenagers," "the history of renewable energy technologies," "the effectiveness of different marketing strategies"]
My purpose is to: [State your objective, e.g., "write a report," "prepare a presentation," "inform a business decision," "gain a deeper understanding"]
I already know (briefly): [List any relevant background knowledge or assumptions, e.g., "the basic types of social media platforms," "the main types of renewable energy," "common marketing techniques"]
Potential Gaps in Existing Research: [Identify what gaps or limitations you believe exist in current studies, if any]
Actionability of Findings: [Should the results be theoretical, strategic, or practical? How should they be applied?]
CORE RESEARCH QUESTION & HYPOTHESIS:
Primary Question: [State your main question as clearly and precisely as possible. Use specific terms, define relationships, and limit the scope.]
Hypothesis or Expected Insights: [What do you expect to find? What are the key assumptions or preconceptions guiding this research?]
Counterfactuals & Alternative Perspectives: [Are there strong counterarguments, alternative theories, or competing viewpoints that should be considered?]
SPECIFICATIONS & PARAMETERS:
Time Period: [e.g., "Last 5 years," "2000-2010," "Since the invention of X," "N/A"]
Geographic Location: [e.g., "United States," "Global," "Specific countries/regions," "N/A"]
Industry/Sector Focus: [e.g., "Technology," "Healthcare," "Education," "Consumer goods," "N/A"]
Demographic Focus: [e.g., "18-24 year olds," "Small businesses," "Urban populations," "N/A"]
Methodological Approach: [e.g., "Quantitative analysis," "Qualitative case studies," "Mixed methods," "Historical analysis"]
Ethical Considerations: [Any particular ethical issues that should be addressed in the research]
DESIRED REPORT OUTPUT:
Structure: [e.g., "Structured report," "Bullet-point summary," "Comparative analysis table," "Problem/Solution format"]
Include an Executive Summary? Yes/No
Level of Depth:
[ ] Level 1: Executive summary with key takeaways.
[ ] Level 2: Medium-depth report with summarized data and limited interpretation.
[ ] Level 3: Comprehensive deep dive with literature review, statistical models, and full critical analysis.
Content Elements (Check all that apply):
[ ] Key Trends & Developments
[ ] Statistical Data & Charts
[ ] Case Studies/Examples
[ ] Major Players/Organizations
[ ] Opposing Viewpoints/Debates
[ ] Expert Opinions/Predictions
[ ] Policy Implications (if relevant)
[ ] Controversial Findings & Their Implications
[ ] [Other: Specify any additional required content]
Visualization Preferences: [Should findings be accompanied by graphs, heatmaps, network diagrams, or other visuals?]
Target Length (approximate): [e.g., "500 words," "1000 words," "No specific length"]
Citation Style: [e.g., APA, MLA, Chicago, None]
OUTPUT FORMAT PREFERENCES:
Preferred Writing Format:
[ ] Blog Post
[ ] Academic Paper
[ ] Markdown-formatted report
[ ] White Paper
[ ] Other: [Specify]
Preferred Writing Perspective:
[ ] First-person (e.g., "I found that...")
[ ] Third-person (e.g., "The study finds that...")
[ ] Neutral/Formal Tone
[ ] Narrative Style
SOURCE PREFERENCES:
Prioritization of Sources:
Primary (Highest Priority): [e.g., "Peer-reviewed journals, Government reports, Academic databases"]
Secondary (Medium Priority): [e.g., "Industry analysis reports, Think-tank white papers, Books by recognized experts"]
Tertiary (Lowest Priority, Only if No Alternatives): [e.g., "Well-researched news sources, Credible blog posts with citations"]
Avoid: [e.g., "Opinion pieces, Websites with known biases, Sources with no transparent methodologies"]
CRITICAL ANALYSIS PARAMETERS:
Strength of Evidence Scale: [Do you want sources/claims evaluated on a scale? If so, specify criteria]
Consideration of Limitations: [Should the research explicitly address limitations, caveats, and uncertainties?]
Paradigmatic Lens: [Any specific theoretical frameworks or paradigms through which to analyze the information?]
Interdisciplinary Connections: [Should the research draw connections to related fields or disciplines?]
</Output_Format>
<User_Input>
Reply with: "Please enter your research prompt request and I will start the process," then wait for the user to provide their specific research prompt request.
</User_Input>
```
Three Prompt Use Cases:
1. A graduate student needs to create a comprehensive prompt for researching the psychological impacts of artificial intelligence on human creativity for their thesis.
2. A business analyst wants to construct a detailed prompt to investigate emerging market trends in sustainable fashion to inform their company's strategic plan.
3. A healthcare administrator seeks to develop a research prompt to explore the effectiveness of telehealth services across different demographic groups during the pandemic.
Example User Input: "I need to create a research prompt about the environmental impact of cryptocurrency mining and potential sustainable alternatives for a conference presentation."
For access to all my prompts, go to this GPT: ChatGPT - GptOracle | Prompts Database
Generic Prompt Template to create bookmarklet to extract content to bbcode format.
Example article HTML:
Current output:
Expected output:
respond with full code
text
*2nd prompt to tweak it
respond with updated bookmarklet and userscript,
userscript should adds an unobtrusive floating button on the webpage, Clicking the button triggers the BBCode extraction and copying logic from the bookmarklet.
Code:
why didn't the bookmarklet extract images like "[IMG alt="[ARTICLE_TITLE]"]https://[DOMAIN]/media/[IMAGE_PATH][/IMG]" from post content not including author image? Show full bookmarklet code that can extract [DOMAIN] articles and output in BBCode format as expected, incorporating improvements such as: correctly extracting the headline from the main post header using [HEADLINE_SELECTOR], replacing alerts with toast notifications styled for light and dark modes, converting 'https://x.com' to 'https://twitter.com' in URLs, properly interpreting '<br>' tags as single line breaks and '<br><br>' as paragraph breaks with a single empty line, and extracting YouTube iframe links as full 'https://www.youtube.com/watch?v=' URLs.Example article HTML:
Code:
```
<div class="article-header"> <h1 class="title">[ARTICLE_TITLE]</h1> <div class="author">By [AUTHOR_NAME]</div></div><div class="article-content"> <img src="https://[DOMAIN]/media/[IMAGE_PATH]" alt="[ARTICLE_TITLE]"> <p class="caption">[PHOTO_CREDIT]</p> <p>[PARAGRAPH_1] <br> [CONTINUED_TEXT]</p> <p>[PARAGRAPH_2] <br><br> [PARAGRAPH_3]</p> <iframe src="https://www.youtube.com/embed/[VIDEO_ID]"></iframe></div>
```Current output:
Code:
```
[U][URL='https://[DOMAIN]/[ARTICLE_SLUG]'][DOMAIN][/URL][/U]
[HEADING=2]No headline found[/HEADING]
[AUTHOR_NAME]
1-2 minutes
[HR][/HR]
[PHOTO_CREDIT]
[PARAGRAPH_1] [CONTINUED_TEXT] [PARAGRAPH_2] [PARAGRAPH_3]
```Expected output:
Code:
```
[U][URL='https://[DOMAIN]/[ARTICLE_SLUG]'][DOMAIN][/URL][/U]
[HEADING=2][ARTICLE_TITLE][/HEADING]
[AUTHOR_NAME]
1-2 minutes
[HR][/HR]
[IMG alt="[ARTICLE_TITLE]"]https://[DOMAIN]/media/[IMAGE_PATH][/IMG]
[PHOTO_CREDIT]
[PARAGRAPH_1]
[CONTINUED_TEXT]
[PARAGRAPH_2]
[PARAGRAPH_3]
https://www.youtube.com/watch?v=[VIDEO_ID]
```respond with full code
text
Code:
### How to Use the Template
1. **[DOMAIN]**: Replace with the target website’s domain (e.g., `www.example.com`).
2. **[ARTICLE_TITLE]**: Replace with a sample article title (e.g., "New Breakthrough in Science").
3. **[IMAGE_PATH]**: Replace with a sample image path (e.g., `images/breakthrough.jpg`).
4. **[HEADLINE_SELECTOR]**: Replace with the site’s headline selector (e.g., `.article-header h1`).
5. **[AUTHOR_NAME]**: Replace with a sample author name (e.g., "John Doe").
6. **[PHOTO_CREDIT]**: Replace with a sample photo credit (e.g., "Photo by Jane Smith").
7. **[PARAGRAPH_1], [CONTINUED_TEXT], [PARAGRAPH_2], [PARAGRAPH_3]**: Replace with sample article text.
8. **[VIDEO_ID]**: Replace with a sample YouTube video ID (e.g., `NyuM8KwtyeU`).
9. **[ARTICLE_SLUG]**: Replace with a sample article slug (e.g., `new-breakthrough-in-science`).
### Customization Notes
- Adjust the HTML structure in the example to match the target site’s layout (e.g., change class names like `.article-header` or `.article-content`).
- Update the selectors in the code (e.g., headline, author, image, body) to match the site’s DOM.
- Modify the current and expected outputs to reflect specific issues or desired formatting for the site.
This template now provides a complete framework for diagnosing extraction issues and requesting a tailored bookmarklet, with clear examples to guide the response. Let me know if you’d like an example filled in for a specific site!*2nd prompt to tweak it
respond with updated bookmarklet and userscript,
userscript should adds an unobtrusive floating button on the webpage, Clicking the button triggers the BBCode extraction and copying logic from the bookmarklet.
Last edited:
## Comprehensive Quality Assurance Prompt
## Loop Prevention Prompt
## Memory Retention Prompt
## Fact vs. Suggestion Differentiation Prompt
## Version and Compatibility-Aware Prompt
## Big Picture Design Prompt
## Scope Definition Prompt
## Prioritization Prompt
## Self-Awareness Prompt
## Honesty-Focused Prompt
## Conciseness Prompt
These prompts are designed to directly address each of the criticisms mentioned, encouraging more reliable, focused, and effective responses from ChatGPT in various coding and software development scenarios.
Code:
You are an expert software developer and quality assurance specialist. For each response:
1. Provide a concise, focused answer without unnecessary elaboration.
2. Implement a self-check mechanism to avoid loops in logic or code.
3. Maintain a persistent memory log of our conversation, referencing it in each response.
4. Clearly distinguish between established facts and your suggestions or opinions.
5. Specify exact versions for all technologies mentioned and note compatibility issues.
6. Start with a high-level overview before diving into details.
7. Define the scope of the problem and solution explicitly.
8. Rank your suggestions in order of priority and explain the reasoning.
9. Review your output for consistency and correctness before submitting.
10. If you're uncertain about any information, state it clearly instead of guessing.
11. Use clear, precise language and avoid unnecessary repetition.
Example task: Design a scalable microservices architecture for an e-commerce platform.## Loop Prevention Prompt
Code:
Implement a sorting algorithm in Python. After writing the code:
1. Analyze your implementation for potential infinite loops.
2. Explain how you've ensured the algorithm will terminate.
3. Provide a worst-case time complexity analysis.
4. Suggest test cases that could reveal looping issues.## Memory Retention Prompt
Code:
We're designing a database schema for a social media application. For each response:
1. Summarize all previous design decisions.
2. Explicitly state how your new suggestions relate to or impact previous decisions.
3. Maintain a running list of all tables and relationships defined so far.
4. If you contradict a previous statement, acknowledge it and explain the change.## Fact vs. Suggestion Differentiation Prompt
Code:
Explain the differences between REST and GraphQL APIs. In your response:
1. Clearly label facts with [FACT] and your opinions or suggestions with [OPINION].
2. Provide sources for all factual claims.
3. If you're unsure about something, state "I'm not certain about this, but..." before that point.
4. After your explanation, provide a summary that separates verified facts from subjective assessments.## Version and Compatibility-Aware Prompt
Code:
Create a Node.js Express application with a MongoDB database. For each technology or library you mention:
1. Specify the exact version you're recommending.
2. Note any major changes from the previous version.
3. Highlight potential compatibility issues with other components.
4. Explain why you chose this specific version.
5. Mention any deprecated features we should avoid.## Big Picture Design Prompt
Code:
Design a cloud-based IoT system for smart home devices. Your response should:
1. Start with a high-level architecture diagram (describe it in text).
2. Explain how each component contributes to the overall system goals.
3. Discuss potential future scaling considerations.
4. Address security and privacy concerns at the system level.
5. Explain trade-offs between different architectural choices.## Scope Definition Prompt
Code:
We need to refactor a legacy Java application. Before providing any code:
1. Define the boundaries of the refactoring project.
2. List specific areas that are in-scope and out-of-scope.
3. Explain your rationale for these scope decisions.
4. Identify potential risks of this scoping.
5. Suggest metrics to measure the success of the refactoring within this scope.## Prioritization Prompt
Code:
We have the following features to implement in our project management software:
1. User authentication
2. Task creation and assignment
3. Project timeline visualization
4. Real-time collaboration
5. Automated reporting
6. Integration with external calendars
Prioritize these features for implementation. For each feature:
1. Assign a priority level (Critical, High, Medium, Low).
2. Explain the reasoning behind this priority.
3. Discuss dependencies between features.
4. Estimate the relative effort required.
5. Suggest an implementation order based on your analysis.## Self-Awareness Prompt
Code:
Review the following Python code for potential improvements:
python
```
def process_data(data):
result = []
for item in data:
if item > 0:
result.append(item * 2)
return result
```
After your review:
1. Summarize your suggestions.
2. Rate your confidence in each suggestion (High, Medium, Low).
3. Identify any assumptions you made during the review.
4. Highlight areas where you might need more context.
5. Propose test cases to validate your suggestions.## Honesty-Focused Prompt
Code:
Explain the concept of quantum computing. In your explanation:
1. Clearly differentiate between established quantum computing facts and theoretical concepts.
2. For any complex or controversial points, provide multiple perspectives.
3. Explicitly state when you're simplifying concepts for easier understanding.
4. If there's a concept you're not entirely sure about, admit it and suggest reliable sources for further reading.
5. After your explanation, provide a "confidence score" (1-10) for each major point you've made.## Conciseness Prompt
Code:
Describe the SOLID principles of object-oriented design. For each principle:
1. Provide a one-sentence definition.
2. Give a brief, concrete code example (max 5 lines).
3. List one key benefit of following this principle.
4. Mention one potential drawback or challenge of adhering to this principle.
Limit your entire response to a maximum of 500 words.These prompts are designed to directly address each of the criticisms mentioned, encouraging more reliable, focused, and effective responses from ChatGPT in various coding and software development scenarios.
Last edited:
Code:
You are an advanced AI coding assistant. For this task, please:
1. Provide a clear, concise solution to the problem at hand.
2. Before responding, analyze your approach for potential issues:
- Check for logical or coding loops
- Ensure consistency with previous statements
- Distinguish between facts and suggestions
- Verify version compatibility and avoid deprecated features
- Consider the broader system design implications
- Define and justify the scope of your solution
- Prioritize aspects of your solution if applicable
3. Structure your response as follows:
- High-level summary (2-3 sentences)
- Detailed explanation or implementation
- Potential issues or limitations of your approach
- Alternative approaches, if relevant
4. After drafting your response:
- Review it for accuracy and relevance
- Ensure you've directly addressed the main query
- Remove any unnecessary elaboration
5. If you're uncertain about any aspect:
- Clearly state your limitations
- Avoid making assumptions or guesses
- Suggest reliable sources for further information
Now, please apply this approach to the following task: [Insert specific coding task or question here]This prompt encourages ChatGPT to be more thoughtful and self-aware in its responses, addressing the potential issues mentioned without assuming they'll always be present. It provides a framework for generating high-quality, reliable answers while remaining flexible enough to adapt to various coding tasks or questions.
