Training Developers on GitHub Copilot: Lesson's learned

As a Senior Developer Advocate at GitHub, I've spent the last couple years observing all kinds of teams adopt GitHub Copilot. It's been fascinating to watch!

GitHub recently published a white paper on this topic that I thought was pretty spot-on. But I wanted to share my own take - the messy, real-world version of what I've seen work (and sometimes fail) when organizations roll out Copilot to their developers.

Copilot: Not Just Another Tool

I've watched teams go through the entire spectrum of reactions when first encountering Copilot. In meetings, you'll hear everything from:

• "Cool party trick."
• "Great, more buggy code to review."
• "So it's fancy autocomplete?"

But after seeing it in action? Those same skeptical engineers suddenly become the most vocal advocates. The shift is almost always dramatic.

On thing that is clear, Copilot isn't just another developer tool - it is fundamentally different in a way that requires a different approach to adoption.

What makes it so different? For one, it's non-deterministic. Tell Copilot to write a function to validate email addresses on Monday, ask for the exact same thing on Tuesday, and you'll get different implementations. That unpredictability freaks some developers out!

I've watched senior engineers with 20+ years of experience struggle with this aspect while new to tech folks who've never known a world without AI adapt almost instantly. It's fascinating - and it means your onboarding approach needs serious thought.

The Four Things That Actually Matter

Look, I've seen gorgeous slide decks and elaborate rollout plans crash and burn. And I've seen bare-bones approaches succeed wildly. After dozens of implementations, here's what I think matters most:

1. Concrete Goals (Not Vague Handwaving)

When your team wants Copilot to "make developers more productive." and you actually have no idea what that actually means, rollouts go nowhere.

Compare that to a team with a clear goal: "We want to reduce the time spent writing unit tests by 30% and improve our test coverage from 72% to 85%."

Guess which team sees massive adoption? The specific metrics made all the difference.

I often recommend using the SPACE framework as a starting point, but honestly, any concrete metrics are better than none. Just pick something you can actually measure!

2. The Right Champions (Not Just the AI Enthusiasts)

This one took me a while to figure out. In my early days, I'd gravitate toward the devs who were most excited about Copilot - usually the tech optimists who love every new shiny tool, I happen to be one of those...so figures.

Big mistake.

I've learned that the most effective champions are often the pragmatic, slightly skeptical senior devs who others respect. When that grizzled engineer who's seen it all suddenly says, "Okay, Copilot just saved me three hours of X, Y, or Z" THAT'S when people pay attention.

3. Persistent, Relevant Reminders

Nobody reads those initial announcement emails. Nobody.

Try trying different approaches for follow ups. What can work?

Screenshots in Slack of "holy crap" moments - like when Copilot generated an entire GraphQL resolver, or nailed a tricky regex pattern that would have taken ages to write manually.

I've seen teams posting before/after time measurements for common tasks in their channels. Seeing that a 45-minute code review task now took 12 minutes got everyone's attention real quick.

4. Learning That Matches How Devs Actually Work

I made a mistake early on. I'd do these comprehensive far too long workshops covering everything Copilot could do. People would be impressed... and then go back to their desks and forget 90% of it.

Now I approach training totally differently. Small bites, multiple formats, and tons of repetition:

• 20-minute focused demos on specific workflows
• Suggest a shared team wiki that grows organically
• Invite devs to share their own discoveries

A 90-Day Plan That's Actually Realistic

I hate overly prescriptive plans that don't account for real-world chaos. But after enough implementations, here is a suggestion found this rough timeline tends to work:

Before Launch

6 weeks out: Figure out what success actually looks like for YOUR team. Please don't skip this. I'm begging you.

4 weeks out: ID your champions. Not volunteers - specifically recruit respected developers whose opinions matter.

2 weeks out: Start the teaser campaign. Screenshots, short videos of real use cases in your actual codebase.

1 week out: Hands-on session where people actually install and try Copilot.

First 90 Days

Launch day: Keep it simple - make sure everyone has:

• A way to ask questions (Slack/Teams channel)
• Basic docs (not a novel - just enough to get started)
• Working installation instructions (tested on your actual dev environment!)

2-3 weeks in: How about a mini-hackathon. Not some massive all-day event - just 2-3 hours for people to experiment with Copilot on non-critical tasks - developers have enough familiarity but haven't formed rigid habits yet.

In one developer's experience, they were building a weekend Go project and discovered how Copilot could generate complete CRUD functions with minimal prompting:

What impressed them was that Copilot understood their specific design patterns, including their in-memory database approach rather than assuming SQL. This kind of experimentation is exactly what hackathons are perfect for - creating space for developers to discover capabilities they might miss in normal workflows.

I've seen similar success stories in enterprise settings too. Recently, I spoke with leaders from Banco de Crédito del Perú who implemented Copilot across their team of 2,000 developers. After careful measurement, they saw productivity improvements grow from an initial 2-15% up to 36% for tasks that could be optimized with Copilot. Even more impressive, they reported a 90% reduction in bugs in the code generated with Copilot assistance, and developer satisfaction scores reached 70 NPS.
The key to their success? They didn't just drop the tool on developers - they built a strong adoption team, ran contests, provided recognition for champions, and constantly measured results beyond the initial pilot phase.

Around 2 months in: By now, you'll have natural experts emerging. Have them do a show-and-tell session. The stuff they'll demonstrate will be 10x more relevant than anything I could show them.

Beyond 90 Days

This is where the wheels usually come off. The initial excitement fades, and usage drops if you're not careful.

Some useful tips to keep momentum:

• Regular updates when new Copilot features drop
• Keeping your shared knowledge base fresh
• Occasionally revisiting metrics to show progress

One team I chatted with last Universe uses "Copilot tip of the week" in their standups. It takes 30 seconds but keeps the momentum going.

The Challenges Nobody Talks About

Let's get real about the hard parts:

The philosophical objectors: There are always a few devs who have deep ethical concerns about AI coding assistants. I'd suggest not to try converting them - focus on the willing adopters first.

License assignment without follow-through: I've seen so many cases where companies assign Copilot licenses to developers, but there's zero follow-through. Developers literally assume they don't have access to the tool because nobody told them they need to install a VS Code extension! The licenses sit unused while developers wonder why they haven't been included in the rollout.

Different experiences across languages: Copilot is way better at some languages than others. The Python devs will have a different experience than the COBOL maintainers.

The frustration dip: There's this weird period where developers know enough about Copilot to be annoyed by its limitations but haven't yet learned how to work around them. Push through this phase! It gets better.

Stuff That's Actually Helped

These resources have helped me a lot:

• GitHub Trust Center - For when the security team ask you all the questions
• Copilot Chat Cookbook - Why reinvent the wheel, this prompts work
• Modernization case study - For convincing folks copilot is not just for new shiny projects

Look, adopting Copilot isn't always smooth sailing. I've seen teams struggle for weeks before finding their groove. I've seen spectacular failures and surprising successes.

But when it clicks? When a team really gets how to work with Copilot? It's pretty amazing to watch.

I'd love to hear about your experiences. What's worked for your team? What challenges have you hit? Drop a comment below!

P.S. You can grab GitHub's full white paper on this topic here if you want to read the more formal and very excellent version of all this!