How to Choose the Right AI Code Assistant for Your Team

In the last two years, we’ve seen the likes of GitHub Copilot, Amazon CodeWhisperer, Tabnine, Replit Ghostwriter, and countless others vying for attention. But selecting the right tool isn’t about picking the most hyped one. It’s about finding the one that integrates seamlessly with your team’s workflow and enhances your software’s quality...not just its quantity.

According to a 2024 survey by Stack Overflow, 70% of developers have incorporated AI code assistants into their workflows. Meanwhile, GitClear’s analysis of AI-generated code found an 8-fold increase in duplicated code when using Copilot, a critical warning for leaders concerned about technical debt. And according to DevOps.com, AIOps adoption is accelerating, with over 60% of organizations now experimenting with AI-driven automation tools.

As AI code generation tools rapidly improve, they’re also creating a minefield of shiny objects and half-baked promises. So, how do you sort the real game-changers from the fluff? By running them through a hard-nosed, practical checklist.

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1. Compatibility with Your Stack

First things first: Can the tool speak your team’s language? Whether you’re deep in Python, JavaScript, C++, or Rust, make sure the tool has expertise in your stack. Many tools excel in JavaScript but flounder in Go or C++.

GitHub Copilot and Amazon CodeWhisperer tend to shine with mainstream languages like Python and JavaScript. Replit Ghostwriter, by contrast, boasts broader language support, but user reviews suggest uneven quality across different languages.

2. Security and Privacy Controls

This is where most AI tools stumble. Can the tool ensure that proprietary code isn’t being siphoned off into some black box model to be repurposed later? Make sure you understand the privacy policy before you even think about integrating it.

AWS CodeWhisperer, for example, is designed with enterprise-grade security in mind. GitHub Copilot, while powerful, raised eyebrows early on for occasionally suggesting licensed code snippets without proper attribution.

3. Adaptability and Learning

Does the tool learn from your team’s unique coding style, conventions, and architecture? Or does it simply regurgitate generic patterns from its training data? Effective tools should adapt over time.

Tools like Tabnine offer local model training, allowing it to customize suggestions based on your specific codebase...something GitHub Copilot lacks.

4. Collaboration Features

Does the tool enhance collaboration or does it simply dump suggestions onto your screen? Tools that can integrate seamlessly with your existing CI/CD pipeline, pull request workflows, and project management systems are worth their weight in gold.

Jira AI Assistant, for instance, provides intelligent suggestions tied directly to your issue tracking, enhancing communication and workflow management.

5. Quality of Generated Code

There’s a difference between code that works and code that’s maintainable. If the tool churns out snippets that pass your tests but violate your design principles, you’ll end up paying down technical debt for years.

GitClear’s analysis found AI-generated code can often violate DRY principles and produce convoluted, difficult-to-maintain code. Engineering leaders must factor this into their decision-making.

6. Cost vs. Benefit Analysis

Is the tool worth the price tag? Some are free, some are subscription-based, and others require enterprise licenses. Match the pricing model with the level of integration and support your team needs.

7. Community and Support

How robust is the user community? Are updates frequent? Are bugs addressed quickly? This is where open-source tools often excel.

8. Documentation and Transparency

How transparent is the tool’s training data and inner workings? Black-box models are harder to trust and even harder to debug when things go wrong.

9. Feedback Loop

Finally, can the tool provide meaningful feedback about its own suggestions? Tools like Replit Ghostwriter are exploring feedback-driven training loops to enhance code quality over time.

It’s not enough for AI tools to be impressive...they have to be compatible, secure, adaptable, collaborative, and transparent. Most importantly, they need to enhance your team’s workflow, not complicate it.

Engineering leaders who evaluate tools with these criteria will find the few that genuinely elevate their teams’ productivity. And those who skip the homework? Well, they’ll be the ones rewriting code by hand long after the AI revolution leaves them behind.

More to come...

Recommended Reads

✔️How to choose the AI coding assistant that works best for your engineering team (Sourcegraph)

✔️A framework for evaluating AI code assistants (Continue.dev)

✔️Training Developers on GitHub Copilot: Lesson's learned (Andrea Liliana Griffiths)

✔️5 Lessons Learned Using GitHub Copilot (CloudFuel)