How to Use AI in an Engineering Team 🤖

The main tools and use cases, with examples from my own experience and that of other tech leaders.

Hey! This is the first of a series of editions that I will write about using AI for your product/engineering work.

I am not exactly early at this. As you may have noticed, we are bombarded everyday with new AI tools, use cases, prompt strategies, and more.

I am not sure how people can be so fast at recommending things, but personally I needed a few months to embed AI into my workflows, iterate on it, figure out what works, and scrap the rest.

I made a point that I wouldn’t write an AI article until I felt I was ready to say something meaningful, and until I could recommend stuff that truly works for me. I feel I am ready now, so here we are!

🏛️ AI Library — new!

For this reason, I have also created a new “AI Workflows” topic in the Refactoring Library. It includes the 2 articles I have written about it, and 10+ tools I recommend.

From now on, it is only going to grow with more resources. If you are a paid subscriber, you will find the link at the bottom of the article 👇

The new AI Workflows section in the Refactoring Library. It is only going to grow!

That said, in this edition we are going breadth-first, analyzing the most effective use cases for a product/engineering team, from communication to coding. As always, this is based on two things:

• Personal experience — I tried all the tools and approaches I list here.

• Chats with other tech leaders — from the Refactoring community and my personal network (which are increasingly becoming the same thing).

So, here is what we will cover today:

• 🛠️ ChatGPT Setup — I don’t use it much, and never from the website.

• 📚 Research — how I use AI for faster and better research.

• 💬 Meetings — how to improve meetings and communication.

• ✏️ Writing — how to improve anything you write.

• 🤖 Coding — tools, pros and cons, and a long list of use cases.

Let’s dive in!