With so many options on the market, and plenty of questions about their outputs, here are some mistakes to avoid when picking an AI coding assistant.
In a matter of months, AI coding assistants went from a promising application of generative AI, to a widely accepted tool within developer workflows.
In this fast expanding landscape there are already a plethora of coding assistants to choose from. How do you know what to look for? Here’s a list of mistakes to avoid as you begin to evaluate what tools could be right for you and your team:
1. Not taking your security review seriously enough
One of the biggest mistakes teams can make when adopting AI coding assistants is neglecting to prioritize security. AI coding assistants often require access to sensitive code repositories and customer data. Failing to conduct a thorough security review can leave your code vulnerable to potential breaches or leaks.
You will want to work with your security colleagues to assess some critical areas about the tools you are considering. Some of the things you’ll need to understand includes how these tools manage:
- Data encryption and storage
- User authentication, access controls, and permissioning
- Compliance with security standards (SOC 2, GDPR, and HIPAA to name a few)
- Security audits and vulnerability assessments
- Privacy policies and data anonymization
- Secure integrations, and end-to-end encryption for communications
2. Assuming that any product will fit into your stack or workflow
Another common mistake is assuming that any AI coding assistant will seamlessly integrate into your existing tech stack. Before choosing a tool, carefully evaluate its compatibility with your preferred programming languages, version control systems, and other essential developer tools.
Integrations are the bridge between the AI assistant and your development world. A strong bridge ensures a smooth flow of information, minimizes disruption, and allows the assistant to function at its best, ultimately maximizing its benefit to your team.
A tight integration can allow an assistant to access your codebase and project data directly, without needing manual data import or manipulation. This ensures the assistant has the most up-to-date information to provide relevant suggestions and analysis. Also, a well-integrated AI assistant can adapt to your team’s conventions, ensuring the code it suggests or generates aligns with your project’s style.
Some AI coding assistants offer advanced features like automated testing or code refactoring. These features often rely on communication with your existing tools. Smooth integration unlocks the full potential of an assistant, allowing it to leverage your development environment for a more comprehensive feature set.
3. Overlook data quality
AI coding assistants are only as good as the data they’re trained on. Training data shapes the assistant’s understanding of coding best practices, syntax, and problem-solving techniques. Poor quality data containing errors, outdated information, or irrelevant code samples, will lead the assistant to make inaccurate suggestions or provide solutions that don’t fit your project’s context.
Furthermore, if the training data is biased towards certain coding styles, languages, or frameworks, the assistant might struggle to adapt to your specific needs. It could also introduce biases into your code, potentially leading to security vulnerabilities or unexpected behavior.
Good training data includes not just code snippets, but also context about the code’s purpose and functionality. This allows the assistant to understand the bigger picture and provide more relevant suggestions. By prioritizing data quality, your assistant can offer more valuable insights and solutions that align with your coding practices and project requirements.
4. Forget about your workflow
When evaluating different AI coding assistants, it’s easy to get lost in the mix of options that serve a myriad of different use cases. It’s important to remember that the goal of an AI coding assistant is to enhance your team’s efficiency.
Implementing an AI coding assistant that doesn’t align with your team’s workflow can hinder rather than enhance productivity. Each development team operates in its own way, with specific coding practices, collaboration methods, and project requirements.
A poorly integrated assistant can disrupt your existing workflow by forcing developers to constantly switch between the assistant and their usual tools. The right tool will become an extension of their familiar environment, allowing them to leverage its features without needing to completely revamp their development process.
Considering your existing workflows allows you to choose an AI coding assistant that complements rather than disrupts your development process. This fosters a smoother transition, optimizes learning, and ensures the assistant’s suggestions directly benefit your codebase without compromising established coding practices.
5. Don’t mistake AI for people
While AI coding assistants are powerful tools, it’s important to remember that they are not human developers.
Mistaking AI for human expertise can lead to unrealistic expectations and disappointment. AI coding assistants excel at automating repetitive tasks, suggesting code improvements, and providing contextual guidance based on patterns in data.
However, they lack the intuition, creativity, and domain knowledge of experienced developers. Acknowledging the strengths and limitations of AI coding assistants will help set realistic expectations and foster a more effective collaboration between humans and machines.
