What to Expect and How to Evaluate AI Contributions

Adopting AI-assisted coding tools is a significant step for any development team. The promise of increased productivity and accelerated workflows is compelling, but it’s essential to approach this new era of software development with a clear understanding of what to expect. Your team’s initial interactions with AI-generated code will set the stage for its long-term success. This guide will walk you through what to anticipate in your first 100 AI commits, how to evaluate the contributions, and how to cultivate a healthy and effective collaboration between your developers and their new AI partners.

Your initial foray into AI-assisted development will likely be a mix of impressive acceleration and surprising quirks. It’s a learning phase for both your team and the AI. Here’s a breakdown of what you might encounter:

• An initial surge in velocity: Teams often experience a noticeable boost in development speed. Repetitive tasks, boilerplate code, and simple functions can be generated in seconds, freeing up developers to focus on more complex problem-solving.
• A new class of bugs: AI-generated code can introduce subtle errors that a human developer might not typically make. These can range from logical flaws to the use of deprecated libraries or insecure patterns. Code reviews become even more critical to catch these AI-specific issues.
• Inconsistent code quality: The quality of AI-generated code can vary significantly. While it can sometimes produce elegant and efficient solutions, it can also generate verbose, inefficient, or difficult-to-read code. This inconsistency is a hallmark of the initial learning curve.
• “Hallucinations” and irrelevant code: AI models can sometimes “hallucinate” and produce code that is completely nonsensical or irrelevant to the task at hand. This is less common with modern tools but can still happen, especially with ambiguous prompts.
• Over-reliance on the AI: There’s a risk that developers, especially those who are more junior, may start to trust the AI’s output implicitly. This can lead to a decline in critical thinking and a missed opportunity to learn the underlying concepts.

Evaluating AI-generated code requires a shift in mindset. It’s not just about whether the code “works” but also about its long-term impact on your codebase. Here are some key areas to focus on during code reviews:

• Correctness and functionality: This is the most basic check. Does the code do what it’s supposed to do? Does it handle edge cases correctly? Rigorous testing is non-negotiable.
• Readability and maintainability: Is the code easy to understand? Does it follow your team’s established coding conventions and style guides? Code that is difficult to read will be difficult to maintain, regardless of who or what wrote it.
• Security vulnerabilities: AI tools can sometimes suggest code with known security flaws. It’s crucial to have automated security scanning in place and to educate your team on common vulnerabilities.
• Performance and efficiency: AI-generated code may not always be the most performant. Look for unnecessary loops, inefficient algorithms, or excessive memory usage.
• Adherence to best practices: Does the code use modern language features and follow established design patterns? Or does it rely on outdated or deprecated practices?

As you review your first 100 AI commits, you’ll start to notice recurring patterns. Some of these are beneficial, while others can be detrimental to your codebase.

Positive Patterns to Encourage:

• Rapid prototyping: AI tools are excellent for quickly scaffolding new features or creating proof-of-concepts.
• Automated test generation: Use AI to write unit tests, integration tests, and end-to-end tests to improve code coverage and reliability.
• Code refactoring: AI can be a powerful assistant for refactoring legacy code, improving readability, and modernizing syntax.
• Natural language to code: Developers can describe the desired functionality in plain English and receive a working code snippet, which can then be refined and integrated.

Anti-Patterns to Avoid:

• “Black box” commits: Commits with large amounts of unreviewed AI-generated code are a major red flag. Every line of code, regardless of its origin, should be understood and vetted by a human developer.
• Ignoring context: AI tools may lack the broader context of your application, leading to solutions that are technically correct but architecturally unsound.
• Diminished learning: Over-reliance on AI can hinder the growth of junior developers. Encourage them to use the AI as a learning tool, not a crutch.
• Inconsistent style: If not properly configured, AI tools can introduce a variety of coding styles, leading to a fragmented and difficult-to-maintain codebase.

To get the most out of your investment in AI-assisted coding, it’s essential to establish clear guidelines and best practices from the outset.

• Start with a pilot program: Roll out AI tools to a small, enthusiastic team first. This will allow you to learn and adapt your processes in a controlled environment.
• Develop clear usage guidelines: Document when and how AI tools should be used. This should include standards for prompting, reviewing, and testing AI-generated code.
• Invest in training: Teach your team how to write effective prompts and how to critically evaluate the AI’s output. Prompt engineering is a new and valuable skill.
• Integrate AI into your existing workflows: Don’t treat AI as a separate, isolated tool. Integrate it into your IDE, CI/CD pipeline, and code review process.
• Measure the impact: Track key metrics to understand how AI is affecting your team’s productivity, code quality, and overall happiness. Look at metrics like cycle time, code churn, and bug density.

While Kinde doesn’t generate code, it provides a solid foundation for building secure and scalable applications, which is essential when incorporating AI-assisted development. By handling the complexities of authentication, user management, and feature flagging, Kinde allows your team to focus on what they do best: building great products.

• Secure by design: Kinde’s robust authentication and authorization features ensure that your application is secure from the start, which is especially important when integrating AI-generated code that may have security vulnerabilities.
• Streamlined user management: By taking care of user management, Kinde frees up development time that can be better spent on core product features, whether they are built with AI assistance or not.
• Feature flags for controlled rollouts: Kinde’s feature flags allow you to safely test new features, including those built with AI-assisted tools, in a production environment before releasing them to all users.

• Where to start with Kinde
• Using feature flags in your application
• Kinde SDKs