Majd Alsado


3 ways I used AI to build a non-AI product

01 May 2023

colive values + openai

When I was impacted by a large layoff 2 months ago, only one thought crossed my mind. Builder time 👷‍♂️ - but what to build?

With the constant stream of GPT-powered products being released, I considered jumping on the trend cycle and trying my hand at building an exciting new AI tool.

But I didn’t want to build something just for the sake of technology. I wanted to build something that would solve a real problem.

Instead of focusing on building an AI product, I want to use AI to help me build a product.

Enter colive values, an idea I had for a platform where modern remote workers can find communities to stay in that align with their values and interests. A project I’ve been interested in for a while, but never had the time to seriously look into.

The story of how that idea came to fruition and how I validated it is for another time.

Today I’d like to share 3 ways I used AI to help me overcome challenges and develop colive values.

🧙‍♂️ #1 - Github Copilot, the Autocomplete wizard

First up is Github Copilot, a GPT-powered tool that integrates into your code editor and offers autocomplete as you type.

How I used it

Having Copilot on is like having a coding partner constantly working alongside me and pushing me along whenever I’m stumped. It analyzes my codebase and constantly provides helpful suggestions and completes my code.

Here it is in action, seamlessly completing code according to a comment I wrote: Github Copilot in action

This helped me focus on the harder problems and build fast.

Lessons Learned

  • Copilot’s ability to take your codebase into context and suggest project-specific code is incredible.
  • It works well ~80% of the time
  • It’s best used for monotonous tasks like writing boilerplate code but falls apart for debugging or refactoring.

#2 👩🏻‍💻 ChatGPT, the trusted consultant

You know ChatGPT, OpenAI’s chat interface to its gpt-3.5 model. It’s the one that’s been making the rounds everywhere.

How I used it

ChatGPT was a valuable tool that helped me build colive values. It served as my consultant in everything from:

  • researching the market, 🕵️
  • bouncing off ideas, 💡
  • drafting user interview plans, 📝
  • crafting or editing marketing messaging, 💬
  • debating application architecture & best practices, 🔐
  • creating one-off scripts to fix data issues, 📊
  • debugging errors, 🐞
  • and so much more.

Lessons Learned

  • ChatGPT is an excellent tool for getting a second opinion, brainstorming ideas, and providing guidance. But it’s not perfect.
  • It’s best used when you carefully instruct it and provide it with context.

For example, when I made a mistake in my file structure, I needed ChatGPT to write a one-off script to fix it.

Here’s how I was able to carefully guide it to generate the exact script I needed

Initial prompt - Giving context and drawing out the problem ChatGPT initial prompt

Getting ChatGPT to regurgitate what I was asking for to validate and reinforce its understanding ChatGPT regurgitating

Tada 🎉 ChatGPT final script

#3 ⚒️ GPT-4 API, the skilled worker

GPT-4 is the world’s most advanced LLM yet, with powerful reasoning capabilities and a massive 1 trillion parameter model, 6x larger than its predecessor GPT-3.

How I used it

During development, I was graciously given early access to the new GPT-4 API and all its power.

So I put it to work on my most crucial task.

Qualifying and classifying my dataset of 400+ communities.

The task wasn’t simple. I needed to skim through each community in my database, understand its vibe, and match it against a list of defined values and interests which best fit it using reasoning and logic.

Before the drop of GPT, I would have just outsourced this task to a freelancer on Upwork. But now I had a new tool in my arsenal.

The process was a messy one, deep in the world of jupyter scripts, pandas dataframes, expensive GPT-4 API calls, and many json files named something like this complete-data-v4-mar24-revised-fix-FINAL-processed.json

But it worked.

Here’s GPT-4 qualifying my dataset in action: GPT-4 in action

That took about a week where I:

  • used prompt engineering to perfect my prompts
  • testing the results on sample datasets
  • & the final hurrah of running the full dataset through my pipeline

Lessons Learned

  • One lesson I learned quickly is that GPT-4 is significantly more expensive than GPT-3.5, about 20x more. Guess which day I ran the full dataset? Expensive GPT-4 Calls
  • I also learned that it’s still not perfect when not instructed carefully. I couldn’t just throw my dataset at it and expect it to work well. I had to spend time engineering my prompts and testing the results.

Wrapping it all up

Using AI tools like Github Copilot, ChatGPT, and GPT-4 was instrumental in building colive values.

These tools helped me speed up development time, reduce errors, and focus on other aspects of development.

AI is no longer just a buzzword; it’s a valuable tool that can help developers build better products faster.

I’m launching colive values this week. If you’re a remote worker seeking coliving communities that align with your values and interests, I encourage you to check it out. It might just have the home away from home you’re looking for!

Check out colive values

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