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The art of prompt coding 🦾


Apps used: / github copilot chat


⚠️️ Before you disregard the idea of prompt coding ⚠️️ Don't! Because everyone will be prompt-coding soon enough.

This is like when humanity stopped using horses for transportation and started using cars. 🐴 👉 🚗

Table of Contents


(Let's dive into it)


Good to know about prompt coding

  • When you restart the LLM, it's like getting a new agent. The new agent may behave slightly differently from the previous one because every agent has its unique personality, just like a person. They also learn differently from each other. Sometimes, a reboot is necessary to start afresh and get back to basics, as the saying goes, "The Ghost in the Machine."
  • Making code modular is essential because LLMs have limited memory and can only handle a maximum length of text. Therefore, it is easier for LLMs to reason about well-structured and modular code, where each module is no more than 100-200 LOC. This is also a good coding practice in general.
  • Being imperative seems to yield better output. For instance, you can use commands like "Make code cleaner,", "Apply styling", "Improve structure," etc.
  • Starting a new chat with a slightly modified prompt can result in better output. - If you are working on something slightly complex, then break it down into smaller chunks. The GPT4 LLM has limitations in computing power, so if it doesn't yield the desired output, try breaking things down further or rephrase your prompt.
  • Do things in steps. For example, "1. The selected cases are from XCTest" "2. Describe in detail what each case does" Follow up with "Be more comprehensive."
  • Ask questions as if you were talking to a person. Highlight the code you want to ask about to give the LLM more context and ask questions like "Is this correct?" "Any typos?" "Is it nil or null?" "Can font color be used in CSS transition?" The LLM will reason about your code and provide you with advice or answers.
  • You can use TextExpander (macOS) to remember and write your most frequent prompts.
  • Prompts marked with a ⭐ emoji are my favorites

Prompt stepping:


Prompting prompts: (Using text expander as the "prompt expander")


Comment code:

Commenting is essential for understanding

The great thing about commenting your codebase is that the LLM understands better how to help you with tasks. Also the code-base will be easier to comprehend, and comments are non-destructive so your code keeps on running as is.

  • Move trailing comments above variables: 1. Move comments above variables 2. Convert to javadoc 3. Remove empty lines
  • Improve comments in a file: suggest comment improvments for this file and add them
    • Alternatie: What does this line do? and add it as a comment
    • Sometimes example code is lost: suggest comment improvments for this file and add them, and keep example code
  • To get more comprehensive comments: add comprehensive comments in this file
  • ⭐ Add code to lines in a method: add inline comments in the selected code
    • Sometimes empty lines are added: add inline comments in the selected code, and dont add empty line breaks
    • Sometimes refrasing helps: add inline comments inside the method or add inline comments to the selected method
    • Seems like the most precise prompt for this is: Add Comment on Current Selection
    • Comment each line and append the comment to the end of the line: add inline comments to the selected code (append comment to the end of each line)
  • Finish a parameter comment: complete the fixme
  • Fix a messy code block comment: fix the messy comment that is selected, clean it up. make it better
  • Sometimes you want the comment block styled append this: add asterisk character to each line
  • Sumarize what a class does: add a general comment regarding what the selected class
  • Sometimes the LLM will arrange comments on a straight line. use this prompt to fix that: dont use a straight line when you add the comments
  • Sometimes the LLM will put the inline comments in the wrong place. above the code instead of after, if so do this: put the comments after the code in the selected code
  • move comments from above lines to at the end of lines: move comment to end of the line
    • More adamant command to move a line from above to the end of a line: take each comment and move it to the end of the line of the code bellow it. To the far right or for each comment move it to the line bellow it to the end at the far right
    • Nuclear option to move a comment from above to the right: 1. take each comment 2. move it to the end of the line bellow it
    • ⭐ Nuclear otion 2: for each line with a comment, take the comment and move it to the end of the line bellow it
  • Make an example of a method an add it to the comment: make an example for this method and add it to the comment
  • To make the LLM give you comments without vertical space use: don't include empty vertical spaces pre or post
  • Comment imports (it's nice to know what they do): comment each import and add the comment to the end of the line for each line or more simple: comment imports (append comment to end of line)
  • Sometimes the LLM will add vertical empty space you dont want, fix that with this prompt: dont add empty vertical space or drop the vertical empty space between lines or more adamant: there are lines in the selection that are empty, remove these
  • Improve comments, but style it with the projects linting file: improve comment for the selected code (restrict formating to the swiftline file the project uses)
  • ⭐ Explode a long call with comments: 💥 add inline comments to the selected code (for just the selected line) (make a new line for each comma) (After using this prompt, the LLM might think you want to explode all lines, a tip to avoid this is to append "dont change any code" in your next prompt)
    • More adamant alternative: 1. Add more vertical space in the selected code by making a new line after each comma. 2. For each new line created append a comment about that line to the end of the line
    • Explode a long call with comments in steps (some calls are stuborn and you have to be more adamant about what you want) : 1. Add more vertical space in the selected code by making a new line after each comma. then select the new lines and prompt: add inline comments to the selected code
  • Add instructions about cvs structure to a comment: add description about the format of the banks.csv then add the selected text to a comment and format it nicely
  • Add instructions about a json structore to a comment: make instructions about json structur brands.json
  • ⭐ Getting an example output from an example in a comment: what would be an example of an output from the code in the selection? (this saves you from running the actual code) follow up wtih: and what would the call be for that output? And the LLM will give you the call to the output 🤯 Adding these to an example in a comment would make the code more readable.
  • Make and example of a selected code and add it to the comments: make an example of the selected method that we can add to the comment, then add that example to the comments (The LLM will also create a description regarding what the example does. Its nice to add this as a comment above the example)
  • Class description: Create a descriptive description for this class and add it to the comment
  • If there is a todo or fixme in a comment. You can ask the LLM to: Solve fixme or solve todo 💥
  • If there is incomplete documentation in the code: finish the docummentation in the selection or simpler: complete param doc
  • Describe the underlaying meaning of a series of method calls: 1. for each method call. 2. Make a short oneliner descripton of what the method does. 3. Append the oneliner as a comment for each line
  • Anchoring preexisting comments: by wrapping your current comments in /**/ the LLM seems to ignore them and wont delete them when you make the LLM add new comments to a piece of code.
  • Add context to a method call: make a comment about the underlaying meaning of the call, make the comment succinct, keep the higher level meaning
  • ⭐ Comment css oneliners: append a comment to the end of the line about what properies is represented, (dont include the actual values) from: padding: 24px 0; to this: padding: 24px 0; /* Sets padding to top/bottom and left/right */ So much more readable ✨
  • Reversing oneliners: make this style into multiple styles This will "explode" a oneliner into respective styles like so: from: font: bold 28px var(--font-family); to: font-weight: bold; font-size: 28px; font-family: var(--font-family);
  • Add documentation on where something is used: - Fixme: ⚠️️ Add doc, regarding where this is used? highlight the text, and prompt: Fix comment. And it will add the reference to where the method is used


Readme markdown etc

  • List to numbred list: change from list to numbered list
  • To improve the structure of a readme markdown file: make suggestions on how to improve this readme doc
  • To make "table of content": can you suggest a markdown index for the selected readme file (sometimes you have to save a file as for the LLM to create the index, or else it will suggest some phyton script instead)
  • To make an index (same as TOC) of a .md file: 1. make a readme table o content of the selected readme file. 2. Make sure the output is in raw markdown syntax
  • Sometimes markdown files are too complex or big. In this case, select partially and prompt with: make index of selection
  • Update a prexisting "table of content" aka toc: update the toc with the new change
  • ⭐ Making a TOC in raw markdown: create toc and format it as raw markdown code
  • Update mit License to latest format: update to latest mit License format
  • Can you make a description for the [insert name of library] library. Creates a nice description for your library
  • Make a full readme based on a class: create boilerplate readme for [insert name your class here]
  • Create examples for your readme based on some class api: create more exmaples on how to use the mockgen class
  • Make an example for one method call: `create an example of using the selected method
  • Improve markdown language: improve language in the selected markdown. output to raw markdown (sometimes the LLM will return the result as normal text, thats why we are adamant about outputting to raw markdown)
  • Make code examples based on a library: can you create some examples for common usecases when using the Account library? then Can you make code examples of these examples you just described?
  • Convert comments to JavaDoc esque comment fencing: convert comments to use javadoc style * (don't include return value) or just: javadoc the comments followed by add * to the left side to add * fencing
  • Make an example that results in a speccific output. The LLM will figure out what the input is. 🐼 add an example using the selected code. The output should be: "panda-will-boat-fair-death-desire-blue-forest" and add it to the comment
  • Sometimes getting raw markdown is tricky, appending the output works everytime: Improve langanguage in this list, output it as raw markdown
  • ⭐ Make documentation that includes documentation regarding method parameters: improve comment for the selected code (add parameter comments) or add parameter doc to comment regarding the selected code
  • Consolidate parameters into a group: consolidate params into parameters: style syntax alternative: consolidate params into parameters in the selected comment
  • ⭐ Make a list of raw urls to a nice descriptive markdown list that describe what the urls link to: convert to markdown links with text as a short oneline about the content in the link (1. output as raw markdown, 2. format as markdown links, 3. description first, 4. link last, 5. as a list)
  • "Numerify" a list: make this list numerical, output in raw markdown

List to table: `



Improve code structure

  • Convert code to tripple qoute multiline text: complete fixme + fix use triple quote string multiline fencing -> """..."""
  • ⭐ Lint / refactor / improve code based on a set of rules / style guide: improve this code with the rules from (Highlight the code you want to improve)
  • Add class type to variables: add type to vars - > let str = "" -> let str: String = ""
  • ⭐ Improving documentation language: improve the language
  • Make numbers more readable: make the numbers more readable by using _ 0.45353535 -> 0.453_535_35
  • Fix indentation in this file: correct the indentation in the selected code, but don't change the content
  • ⭐ Indent json, yaml, html, css etc: indent the selection properly (don't delete comments)
  • ⭐ Improvoving code: Anything I could improve in this code? This will give you suggestions of how to improve code. Like suggesting global variables in css. Or how to name things better etc.
  • Transform code into a oneliner: (select some code) then prompt: make oneliner:
border-style: solid;
border-width: 4px;
border-color: var(--link-color); 

into this: border: 4px solid var(--link-color);

  • Asking which design pattern to used on a OTP utlity class: what design pattern could we use to refactor this code -> The code in this file is already using the Factory Method design pattern to create OTP accounts. However, the code could be refactored to use the Builder design pattern to make it more flexible and easier to read. The Builder pattern would allow the code to create OTP accounts with optional parameters, making it easier to create accounts with only the necessary parameters. Additionally, the Builder pattern would allow the code to create OTP accounts with default values for optional parameters, making it easier to create accounts with default values.


Refactor code

  • If you have some Fixme or Todo comments in a comment. You can highlight them and prompt: complete fixme and the LLM will try to complete the tasks. If they are not too complex.
  • Converting From one framework to another: (From UIKit to swiftUI) how would this look like in swiftui?
  • Invert and replace colors: whats the inverted color values of the selection? then replace the values. This will invert any color in a text selection and replace the values
  • ⭐ Combine tasks (if they are connected and simple enough) like: structure this with each case on each line and comment the line, put the comment at the end of the line (for switch, case, enum etc)
  • Restructure a one-liner to use more vertical space: 1. restructure the selected line to use more vertical space. 2. comment each line 3. append the comment to the end of the line
    • More adamant restructure prompt in steps (if the above fails): restructure the selected line to use more vertical space. then add inline comments to the selected code, append comment to end of line, not above it ⚠️️ (A good idea could be to get an overall comment for the higherlevel meaning of a line before it is "exploded") ⚠️️
    • Even more adamant: 1. Add more vertical space in the selected code by making a new line after each comma. 2. For each new line created append a comment about that line to the end of the line (append comments to the end)
    • ⭐ Nuclear option: Add more vertical space in the selected code by making a new line after each comma. Then make a meaningful comment about each line, and add that comment to the end of the line to the far right
  • Add type to a variable: add the type to the variable I.e: var str = "Hello" after -> var str: String = "Hello"
  • ⭐️ Refactor a ternary to if else: refactor the ternary to use if else
  • Refactor if else to a ternary: Refactor if else to a ternary
  • Renaming classes: Can you suggest some alternative names for the selected class name? followed by some more?
  • ⭐️ Simplify code: is it possible to write the selection simpler? alt: compress selected styles if you want to add a comment: compress selected styles, and add comment to each value describing what they are, append the comment at the end of the line (the last part is key, or css wont be parsed correctly) from this:
padding-left: 24px;
padding-right: 24px;
padding-top: 32px;
padding-bottom: 32px;

into this:

padding: 32px 24px;
  • ⭐ And adding comment that make it more readable: 1. Compress into one style. 2. Append a comment to the end of the line about what properies is represented, (don't include the actual values)
  • ⭐ Format comments in javadoc fencing: use javadoc style commenting fence or if that fails try: Format the comments like this:


Extra tips and tricks

  • Asking about a css thingy you have forgotten: I want to target the first .page item with a custom padding. How do I do that? -> .page:first-of-type { padding-top: 20px; } (much faster than going to StackOverflow and wading through unrelated code)
  • Extract values from text. select all the 4 string values output: A list of all text that is 4 in length.
  • Replace values in text: Add one of the values here: "I5Fl, d8j8, XsYY" to the selected text, in place of all id value
  • The LLM is your buddy. Ask it trivial stuff like: whats the link to Monteserrat on google fonts? -> Here's the link to Montserrat on Google Fonts: or is the correct url to feather icons: -> Yes, that is the correct URL for Feather Icons.
  • Add code blocks: add jekyll code that gets a value from general.yml with key download-universal output: {{ }}
  • Ask questions about code like: is the style that is selected vertical or horisontal? style: column-gap: 12px; answer: The column-gap property is used to set the gap between columns in a multi-column layout. So, it is a horizontal style.
  • Find nicer fonts: Recommend fonts are better than we have now? (Using Montserrat) recomends: open sans (similar in style to Montserrat, but has a slightly softer look.) , roboto (clean and modern look, similar to Montserrat.), lato (known for its readability and clean design). Follow up with I want to add link href with all open sans fonts. What does the html element look like for that? to add these fonts to your website
  • Highlight a link in a file. And ask cursor to can you make one sentence summary of the selected link?. The LLM will create text that describes what the website in the link is about. Shorter: whats the selected link about
  • You can generate a commit message based on the changes made: make a commit message based on the changes made or make a commit message
  • Add logic to cryptic .xml config files like .plist: add a comment aboute what each lines does and append it to the end of the line
  • Create random strings with terminal: I need a short random string of 4 in length. how can I generate that with terminal for mac
cat /dev/urandom | LC_CTYPE=C tr -dc 'a-zA-Z0-9' | head -c 4; echo
  • Interpreting code succinctly: describe what this line does, high level to add that description as a comment: describe what this line does, high level, then add that description as a comment to the end of the line (this works on multiline selections as well) alternativly: add a comment to end of each line about what each line represents
  • Create exotic and bright debug colors: rename this to be one two three etc. but keep the prefix. And use a random named color from standard web colors. make the list 30 items long. use more exotic colors. that are strong and bright. but make sure they are named follow up with use real numbers instead of written numbers if you want real numbers from this: --debug-bg-one: blue; to this:
--debug-bg-one: IndianRed;
--debug-bg-two: SeaGreen;
--debug-bg-three: DeepPink;
--debug-bg-four: DodgerBlue;
/*...and so on*/
  • Add comments what each line in a .gitignore file: can you add comments to the selected .gitignore file
# Ignore macOS-specific files

# Ignore build artifacts

# Ignore package dependencies

# Ignore Xcode project files

# Ignore Xcode user-specific data


Ways of thinking about prompt-coding

  • Sometimes going right for a solution too quick just ends up failing.
  • A strategy is sometimes to get something simple working, and then ask the LLM to iterate on that instead.
  • Then step by step add more complex functionality.
  • And sometimes Its better to start over with a different starting point. Like code from another language and ask the llm to convert that to the desired language. and then iterate on that like the gif bellow:


The resulting script:

  1. you need to intall python3 first
  2. in terminal cd ~/desktop/
  3. then python3
import json
import urllib.request
import ssl

username = input("Enter your GitHub username: ")

url = f"{username}/events"
context = ssl.create_default_context()
context.check_hostname = False
context.verify_mode = ssl.CERT_NONE

req = urllib.request.Request(url)
req.add_header("Accept", "application/vnd.github.v3+json")
with urllib.request.urlopen(req, context=context) as response:
    events = json.loads(

lines_added = 0
lines_deleted = 0
for event in events:
    if event["type"] == "PushEvent":
        for commit in event["payload"]["commits"]:
            commit_url = commit["url"]
            req = urllib.request.Request(commit_url)
            req.add_header("Accept", "application/vnd.github.v3+json")
            with urllib.request.urlopen(req, context=context) as response:
                commit_data = json.loads(
                lines_added += commit_data["stats"]["additions"]
                lines_deleted += commit_data["stats"]["deletions"]

print(f"Total lines of code added by {username}: {lines_added}")
print(f"Total lines of code deleted by {username}: {lines_deleted}")
print(f"Net lines of code added by {username}: {lines_added - lines_deleted}")

Conformed refactoring

Refactor based on external ideas / concepts

Refactor something based on the instructions of a github library readme file: Use this example: and use the same idea on the selected code. But only on the selected code not the entire file follow up with: drop the button value, just return the with closure then drop the return then also use .init directly instead of the class name feathericonbutton

From this:

let button = FeatherIconButton(icon: .xCircle, size: .small)
button.addTarget(self, action: #selector(removeCell), for: .touchUpInside)
return button

to this:

with(FeatherIconButton(icon: .xCircle, size: .small)) {
   $0.addTarget(self, action: #selector(removeCell), for: .touchUpInside)

Another name for Conformed refactoring could be: "Constrained refactoring" or "Rule-bound refactoring"

I think of it as "reservoirs of information". We can then query this information with a question we have. In code, I refer to this as "conformed refactoring", where I can ask my "code bot" to rewrite something based on the concept or API of a GitHub README file. Essentially, a book is like a "game cartridge" that you plug in. You can then ask for the top 10 relationship books about the problem you have, and get coached by the books. The chatbot acts as the mediator. Soon, you will be able to do this with Chat GPT-4, now that they are opening up for external sources.


This will allow you to get help with a single line of text in the Chat GPT-4 app on your iPhone. For example, if you are having trouble with XYZ, you can ask "According to the book 'Men Are from Mars, Women Are from Venus', how should I solve my problem?".


You can also ask external link how to do things like: in the selected link there is a description regarding updating submodules. whats the commandline call it uses? link: output: The article in the selected link describes how to update submodules using the following command: git submodule update --init --recursive 🤯

Check your new productivity:

Proof is in the pudding

  1. save this as on your desktop.
  2. then in terminal cd~/desktop
  3. then in terminal bash -x
  4. Type in your github user-name 👉 And it will spit out your commit count for today

# Prompt the user to enter their GitHub username
read -p "Enter your GitHub username: " username

# Get the total commit count for the user
commit_count=$(curl -s "$username/events" | grep -c "PushEvent")

# Print the total commit count
echo "Total commit count for $username: $commit_count"

Or check out Github_stats_v2 (With added streak feature)

Here is how the v2 script looks like in action:


Bug fixing

Debugging error and fixing bugs

Coming soon


Resources and links


Improvments and todos

  • Structure prompts in a table with TextExpander shortcut. Bonus: Make github copilot chat create the table 🤯
  • Make a bash script that gets LOC for the day for a github user. Without using JQ or python etc. JSON parsing is hard without these. So figure out how to parse json with something else. 🚫
  • Structure TOC into tree structure, group things etc
  • Make v3 of the github script. More stats, avarages etc. Format the output in a table like structure. 👈


Got feedback or suggestions? DM me @ or on linkedin

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eonist commented Feb 7, 2024

import logger from use main branch. us the latest spm branch syntax

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eonist commented Feb 8, 2024

Adding multiple packages: add local packages XYZ, ABC, DEF, 123

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eonist commented Feb 9, 2024

Editing Github actions:

  1. schedule a run every thursday at 18 utc+1
  2. add it to the .yml file

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eonist commented Feb 9, 2024

Adding new rows in a markdown table:

  1. add a similar item in the markdown table with this data:

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eonist commented Feb 9, 2024

Solving Github actions errors:

im getting this error: Run swift build -v
error: 'localnotification': package 'localnotification' is using Swift tools version 5.9.0 but the installed version is 5.7.1
Error: Process completed with exit code 1.

Copiliot fixes your .yml file

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eonist commented Feb 9, 2024

Reusing code by making closures for unit testing:

  1. make a closure of the selected code, where I can insert a credential
  2. make the param CredentialKind

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eonist commented Feb 16, 2024

Github copilot is great at writing unit tests from code api / methods

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eonist commented Feb 16, 2024

adding columns in markdown: add another column with

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eonist commented Feb 17, 2024

Making coolers with copilot: "create a nscolor object whose grayscale value is 2/3 and whose alpha value is 1.0." output: let color = NSColor(white: 2.0/3.0, alpha: 1.0)

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eonist commented Feb 17, 2024

You can also paste in a long list of Rob or hex values, and copilot will format it in whatever format you want etc

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eonist commented Feb 18, 2024

you see how the selected color is made. make the same for these: lightGray

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eonist commented Feb 18, 2024

Suggestions to dividing the text into chapters:

  • Here are some suggested chapter titles for the given GitHub gist:
  • Introduction to AI coding prompts
  • Debugging and bug fixing in AI programming
  • Resources and links for AI development
  • AI code editor:
  • Github Copilot chat:
  • Hosting gifs:

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eonist commented Feb 18, 2024

book title ideas:

"Coding the Future: A Journey into AI Development"
"Digital Minds: Exploring the World of AI and Coding"
"AI Unleashed: The Power of Code and Innovation"
"The AI Coder's Guide: Navigating the World of Machine Intelligence"
"Decoding AI: A Comprehensive Guide to Artificial Intelligence and Programming"

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eonist commented Feb 18, 2024

Some potential new chapter ideas:

  1. AI Debugging and Error Fixing
    Provide a comprehensive guide on debugging techniques and best practices for AI projects, including common errors and how to resolve them.
  2. AI Code Editors and Tools
    Compare and contrast popular AI code editors, such as and GitHub Copilot, and discuss their features, pricing, and use cases.
  3. AI Hosting and Deployment
    Explore various hosting options for AI projects, including free and paid services, and provide guidance on choosing the best option based on specific needs.
  4. AI Research and Resources
    Discuss the use of AI for research, including tools like, and provide a curated list of resources for AI researchers.
  5. AI Project Management and Collaboration
    Offer tips and best practices for managing AI projects, including collaboration tools, version control, and project planning.
  6. AI Ethics and Responsible AI
    Discuss the importance of AI ethics and responsible AI, and provide guidance on how to ensure that AI projects are developed in a responsible and ethical manner.
  7. AI and the Creative Process
    Explore the use of AI in creative fields, such as writing, art, and music, and provide examples of successful AI-driven projects.
  8. AI and Education
    Discuss the use of AI in education, including tools for personalized learning, adaptive assessments, and AI-driven tutoring systems.
  9. AI and the Future of Work
    Discuss the potential impact of AI on the future of work, including the creation of new jobs, the displacement of existing jobs, and the need for retraining and upskilling.
  10. AI and the Environment
    Discuss the use of AI for environmental applications, including tools for monitoring and managing natural resources, predicting climate change, and reducing waste.

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eonist commented Feb 25, 2024

Asking for second opinions in various chatbots is key to not go down the wrong rabbit holes. and are pretty good atm. Asking both the same question can yield different solutions, and asking again after refreshing the webpage can also yield different solutions.

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eonist commented Feb 29, 2024

✨ convert the selected code to this structure : ..insert custom code format here..

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eonist commented Mar 2, 2024

Current workflow:

  1. Describe the code problem on or
  2. Choose the best solution or upvote in suggested follow-on suggestions.
  3. Modify the answer on GitHub Copilot in VSCode. ✅

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eonist commented Mar 5, 2024

On prompt coding via

Best Practices for Prompt Coding with AI


Prompt coding is an essential aspect of using AI models effectively. Crafting well-designed prompts can significantly improve the quality of AI-generated responses. In this report, we will explore some of the best practices for prompt coding with AI.

Understanding the Prompt

To ensure effective prompt coding, it is crucial to have a clear understanding of what the prompt is asking and how the AI model is likely to interpret it. Here are some key points to keep in mind:

  • Be specific: Clearly define what you want the AI model to generate. Vague or ambiguous prompts may lead to inaccurate or irrelevant responses.
  • Consider the context: Understand how the AI model interprets the context of the prompt. For example, some models may generate responses based on the specific context mentioned in the prompt, while others may rely more on general knowledge.
  • Use explicit instructions: Provide explicit instructions to guide the AI model. For example, if you want the model to give pros and cons, explicitly state that in the prompt.

Designing Effective Prompts

Crafting effective prompts involves designing them in a way that elicits the desired response from the AI model. Here are some best practices for designing prompts:

  • Start with an introduction: Begin the prompt with a brief introduction to provide context for the AI model. This helps set the stage for the desired response.
  • Use example-based prompts: Providing examples in the prompt can help the AI model understand what kind of response is expected. This can be particularly useful for complex prompts.
  • Break down complex prompts: If the prompt is complex or multi-part, consider breaking it down into simpler sub-prompts. This can make it easier for the AI model to generate accurate responses.

Structuring the Prompt

The structure of the prompt can influence the quality of the AI-generated response. Here are some tips for structuring prompts effectively:

  • Be concise: Keep the prompt concise and to the point. Avoid unnecessary or redundant information, as it may confuse the AI model.
  • Use proper formatting: Clearly separate different sections or sub-prompts using appropriate formatting techniques, such as bullet points or headers. This helps the AI model understand the structure of the prompt.
  • Include relevant context: If the prompt requires specific context or background information, include it in a concise and clear manner. This helps the AI model generate responses that are aligned with the desired context.

Additional Resources

For further reading on prompt coding with AI, here are some helpful resources:

Remember, effective prompt coding is a combination of understanding the prompt, designing it effectively, and structuring it appropriately. By following these best practices, you can enhance the performance of AI models and achieve more accurate and relevant responses.

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eonist commented Mar 6, 2024

Styling design 1.make the tappable area twice the size of the fill area, 2.set the speed of this animation to 0.04, 3.add a stroke

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eonist commented Mar 6, 2024

Great for package.swift manifest configuration: require macOS 14.0 or newer

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eonist commented Mar 6, 2024

Editing complex markdown tables: "add to this markdown table"

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eonist commented Mar 7, 2024

Making things public and ready for a swift package on GitHub: "make things public, keep all comments"

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eonist commented Mar 7, 2024

refactoring tip: "make fileprivate static lets of these variables, use the name from the comment above each init"

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eonist commented Mar 8, 2024

✨ Really powerful prompt: 1. Select some code you want some other code to look like. Copy it. 2. Select the code you want to structure and ask: structure the selected code like this: (paste the code you want to structure to) sometimes you need to be more adamant: "refactor selected code to this structure. (keep the code, just change structure)"

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eonist commented Mar 10, 2024

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eonist commented Mar 13, 2024

Holy prompt: "Do the fixme"

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eonist commented Mar 13, 2024

...and keep the comments

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eonist commented Mar 19, 2024

God prompt: "fix the syntax error"

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