AI is changing developer work. Here are three skills to strengthen.

AI is changing how developers work and apply their skills. Writing code is still essential, but developers increasingly need to know how to direct AI, evaluate its output, communicate tradeoffs, and make sound technical decisions.人工智能正在改变开发人员的工作方式及其技能的应用。编写代码依然至关重要，但开发人员越来越需要掌握如何引导人工智能、评估其输出、沟通权衡方案并做出合理的决策。

The good news? You can start preparing today. Here’s where to focus:好消息是，你现在就可以开始准备了。以下是重点关注方向：

Tip #1: Learn to direct AI, not just use it.建议一：学会引导人工智能，而不仅仅是使用它。

AI is changing what execution looks like. Increasingly, great execution means defining the problem clearly, providing the right context, evaluating AI-generated code, and deciding what’s ready to ship. As AI agents take on more of the implementation, these skills become even more valuable.人工智能正在改变执行工作的面貌。优秀的执行力越来越体现为：清晰地定义问题、提供正确的背景信息、评估人工智能生成的代码，以及决定哪些内容可以交付。随着人工智能代理承担起更多的实现工作，这些技能将变得愈发宝贵。

For example, imagine you’re asked to add a new authentication flow.例如，假设你需要添加一个新的身份验证流程。

A traditional workflow might look like this:传统的工作流程可能如下所示：

Task: Add authentication  

→ Create branch  
→ Write code  
→ Run tests  
→ Open pull request

As AI tools become more capable and more deeply integrated into day-to-day workflows, those same skills can help you coordinate multiple AI agents. Your workflow might look more like this:随着人工智能工具变得更加强大并深入集成到日常工作流中，同样的技能可以帮助你协调多个智能体。你的工作流程可能会变成这样：

Workspace: Add authentication  

Agent 1 ✓ Authentication ready for review 
Agent 2 ✓ Documentation draft ready  
Agent 3 ✓ Test suite ready

Notice what changed. You’re still responsible for the outcome, but you’re spending less time implementing every piece yourself. Instead, you’re defining the work, reviewing outputs, and making the technical decisions that bring everything together.注意其中的变化。你仍然对结果负责，但你花在亲自实现每一个环节上的时间减少了。取而代之的是，你将重心放在定义工作、审查输出以及做出将一切整合在一起的技术决策上。

Takeaway: Learn to direct AI agents.要点：学会引导人工智能代理。

Start your first agent session >开始你的第一次代理会话 >

Tip #2: Don’t trust AI’s first answer建议二：不要轻信人工智能的第一个答案

AI can generate impressive solutions in seconds, but the first answer isn’t always the best. Your experience writing clean, maintainable code can help you evaluate AI’s output.人工智能可以在几秒钟内生成令人印象深刻的解决方案，但第一个答案并不总是最优的。你编写整洁、可维护代码的经验，有助于你评估人工智能的输出。

Ask a second AI model to critique the first model’s work, then use your own judgment to evaluate both responses.让第二个模型去批判第一个模型的工作，然后利用你自己的判断力来评估这两个回答。

Here’s a prompt that shows what that might look like in practice:这里有一个提示词示例，展示了在实践中如何操作：

"Write a SQL query that returns each customer's most recent order." 
↓ 
AI Model #1 
✓ Generates the query 
↓ 
AI Model #2 (Critique) 
⚠ Doesn't handle duplicate timestamps 
⚠ Missing index recommendation 
⚠ May perform poorly on large tables

Different AI models have different strengths and blind spots. That’s why GitHub Copilot’s built-in Rubber Duck agent uses a second model to critique plans, code, and tests before you move forward. A second perspective often catches issues the first model misses.不同的人工智能模型有不同的优势和盲点。正因如此，GitHub Copilot 内置的 Rubber Duck 代理会在你继续下一步之前，使用第二个模型来审查计划、代码和测试。第二个视角往往能发现第一个模型遗漏的问题。

Takeaway: Trust AI enough to use it but not enough to skip review.要点：信任人工智能以便使用它，但不要信任到跳过审查的程度。

Tip #3: Use AI to solve bigger problems建议三：利用人工智能解决更大的问题

When AI saves implementation time, developers can use that time to tackle broader problems: understanding customer needs, evaluating tradeoffs, designing better systems, and making technical decisions that AI can’t make for them.当人工智能节省了实现时间时，开发人员可以利用这些时间去处理更广泛的问题：理解客户需求、评估权衡方案、设计更好的系统，以及做出人工智能无法代劳的技术决策。

For example:例如：

Issue #4821 
Title: Add dark mode 

AI 

✓ Build implementation 
✓ Generate tests 
✓ Update documentation 

Developer checklist 

☐ Validate customer problem 
☐ Review architectural tradeoffs 
☐ Check accessibility 
☐ Define success metrics 
☐ Approve solution 

As AI takes on more implementation work, the skills that distinguish great engineers become even more important: exercising sound judgment, balancing tradeoffs, and solving the right problems.随着人工智能承担更多的实现工作，区分优秀工程师的技能变得更加重要：运用合理的判断力、平衡利弊权衡，并解决真正重要的问题。

Takeaway: Let AI handle more of the implementation so you can spend more time building the judgment that helps teams make better decisions.要点：让人工智能处理更多的实现工作，这样你就可以花更多时间培养帮助团队做出更好决策的判断力。

The bottom line总结

As developer workflows evolve, learning to work effectively with AI—and strengthening your own technical judgment—can help you adapt.随着开发人员工作流的演变，学会与人工智能有效协作，并加强你自己的技术判断力，将有助于你适应这一变化。

Put these ideas in practice >将这些想法付诸实践 >

Written by 作者

Gwen Davis

@purpledragon85

Gwen Davis is a senior content strategist at GitHub, where she writes about developer experience, AI-powered workflows, and career growth in tech.