Best practices and tips for developers to integrate AI tools into their workflows

Watch this video to learn about AI capabilities, limitations, and best practices.

Brent Laster, a global trainer, author, and speaker on open source technologies, sat down with the All Things Open team and explored the evolution of development environments, the impact of AI on software development, and practical advice for developers looking to integrate AI tools into their workflows.

Key takeaways

Evolution of development environments

• Development practices have shifted towards optimizing for speed, consistency, cost, and scalability, particularly with the rise of cloud computing, containers, and CI/CD pipelines.
• Modern approaches now emphasize microservices and stateless architecture, making systems more resilient and manageable.

AI as a tool

• AI should not be viewed as a replacement for developers but, as a new team member who requires guidance and oversight.
• Treat AI-generated code with the same scrutiny as you would any work from a new hire, ensuring thorough code reviews and testing.

Understanding AI’s capabilities and limitations:

• AI tools function primarily as prediction engines; they can assist with coding but do not grasp the full context or intent behind tasks.
• Training and familiarity with AI tools are essential to maximize their utility and effectiveness.
• It is essential to provide clear context when working with AI tools to achieve better results.

Experimentation with AI

• Developers are encouraged to experiment with various AI tools like GitHub Copilot and Codium, as well as local language models using platforms like Ollama and Hugging Face.
• Hands-on experience is key; try different models and understand their capabilities through trial and error.

Stay updated

• Be aware that AI language models may be trained on data that is not current. Always check the latest versions of frameworks or languages to avoid deprecated code.
• Regularly asking AI tools about the current version of your tech stack can provide important context.

Getting started with AI

• For those new to AI, starting with resources like Hugging Face can be beneficial. Explore their model cards to find suitable AI models and implementations.
• Always treat AI as a tool that requires guidance. Provide context and conduct reviews to ensure the quality of AI-generated outputs.
• Be aware of the limitations of AI and supplement its use with current knowledge about the technologies you’re working with.

By adopting these practices, developers can effectively integrate AI into their workflows, leveraging its capabilities while ensuring the quality and relevance of their work.

More from We Love Open Source

• Getting started with Llamafile tutorial
• How Netflix uses an innovative approach to technical debt
• Evolving DevOps with productivity and improving the developer experience
• Harness the power of large language models part 1: Getting started with Ollama