Understanding how to integrate AI without disrupting 'flow' is crucial for productivity. Effective AI tools should focus on removing toil and providing contextual assistance rather than replacing human judgment or forcing unnatural interaction patterns like constant chat-switching.
At GitHub, we hear questions all the time that probably sound familiar to you:
These questions are real and valid. I did a livestream for our regularly scheduled Rubber Duck Thursdays (which you should check out on GitHub’s YouTube, Twitch, and/or LinkedIn weekly!) with Dalia Abo Sheasha, Senior Product Manager for Visual Studio, to talk about these things and more!
Check it out, or read on for the highlights:
If you ask most software engineers what they most want out of a tool, the answer usually isn’t “more automation.” Most developers are looking for a smoother, less interrupted path toward flow, that state where code and ideas come easily. It’s a fragile state.
We’ve seen again and again that anything causing context-switching (even a well-meaning suggestion) can snap that flow. With that in mind, at GitHub, we design and test our AI features where developers already work best: in their editor, the terminal, or the code review process. And we give developers ways to tune when, where, and how these tools make suggestions.
Your tools should support your workflow, not disrupt it. We want AI to help with the stuff that gets you out of flow and keeps you from building what matters. If a feature doesn’t truly make your coding day better, we want to know, because the only good AI is AI that actually helps you.
It’s tempting to believe that everything should be chat-driven. There’s power in asking “Can you scaffold a template for me?” and getting an instant answer. But forcing all interaction into a chatbox is, ironically, a fast path to losing focus.
I’m required to switch my attention off my code to a different place where there’s a chat where I’m talking in natural language. It’s a huge burden on your brain to switch to that.
Dalia Abo Sheasha, Senior Product Manager, Visual Studio
For many developers, chat is better suited to on-demand tasks like code explanations or navigating frameworks. If chat panels get in the way, minimize or background them. Let the chat come to you when you actually have a question, but don’t feel pressured to center your workflow around it.
User data and developer interviews show us that effective AI empowers developers, but doesn’t replace their judgment.
Time and again, developers have told us what they really want is a way to skip repetitive scaffolding, boilerplate, and tedious documentation, while still holding the reins on architectural decisions, tricky bugs, and business logic.
As I explained during the stream: Focus on different behaviors for different audiences. Senior developers already go fast, but you’re trying to change their established behavior to help accelerate them. But for students, you’re training a brand new behavior that hasn’t been fully defined yet.
Use AI-generated explanations to deepen your own understanding. They should never be a replacement for your own analysis.
Cassidy Williams, GitHub Developer Advocate
And we want them to learn because the students—the early-career developers of today—are the senior developers of tomorrow, and everything’s changing.
What stage are you in in the learning process? If you are at the very beginning and you are learning syntax and the fundamentals of programming, use it to explain the fundamentals so you can have that strong foundation.
Dalia Abo Sheasha
AI truly shines when it works alongside you rather than in front of you.
Developers tell us the most valuable AI experiences come from suggestions that surface contextually, such as suggesting a better function or variable name when you initiate a rename, or autocompleting boilerplate. In these moments, the AI tool feels like a helper handing you a useful snippet, not an intrusive force demanding attention.
Most AI assistants offer ways to adjust how often they pop up and how aggressive they are. Take a few minutes to find your comfort zone.
AI should be your tool, not your replacement. AI tools should empower you, not take over your workflow. We want AI to remove tedium by suggesting improvements, writing docs or tests, catching issues… not to disrupt your creative flow or autonomy.
The most critical ingredient in software is still the human developer: your insight, judgment, and experience.
Not every AI feature lands well. Features that interrupt editing, flood the screen with pop-ups, or “help” while you’re adjusting code in real time usually end up disabled by users, and often by us, too.
There is definitely a lot of AI fatigue right now. But there are also such good use cases, and we want those good use cases to float to the top … and figure out how we can solve those developer problems.
Cassidy Williams
If a suggestion pattern or popup is getting in your way, look for customization settings, and don’t hesitate to let us know on social media or in our community discussion. Product teams rely heavily on direct developer feedback and telemetry to adjust what ships next.
Whether it’s through beta testing, issue feedback, or direct interviews, your frustrations and “aha!” moments drive what we prioritize and refine.
If you have feedback, share it with us! Sharing your experiences in public betas, contributing to feedback threads, or even just commenting on what annoyed you last week helps us build tools you’ll want to use, not just tolerate. Your input shapes the roadmap, even in subtle ways you might not see.
To get practical benefit from AI tools:
AI coding tools have enormous potential, but only if they adapt to developers. Your skepticism, high standards, and openness help us (and the entire software industry) make meaningful progress.
We’re committed to creating tools that let you do your best work, in your own flow, right where you are.
Together, let’s shape a future where AI enables, but never overshadows, the craft of great software development.
The post What AI is actually good for, according to developers appeared first on The GitHub Blog.
Continue reading on the original blog to support the author
Read full articleThis demonstrates how to use AI and automation to solve 'tragedy of the commons' issues like accessibility that cross team boundaries. It provides a blueprint for building agentic workflows that enhance human productivity and ensure critical user feedback is never lost in the backlog.
AI-driven code reviews are reaching massive scale, shifting from pattern matching to agentic reasoning. For engineers, this means faster PR cycles and higher-quality feedback, as tools now prioritize architectural context and actionable signals over generic linting or noise.
This article highlights how structured AI integration in production workflows bridges the global talent gap. For engineers, it demonstrates practical strategies for using AI to navigate legacy systems, improve test coverage, and accelerate onboarding in high-stakes environments.
These events provide engineers with hands-on experience in AI-assisted development, helping them integrate tools like GitHub Copilot into their daily workflows. Staying updated on AI tools is crucial for maintaining productivity and efficiency in a rapidly evolving software landscape.
