AI Engineering Productivity Cookbook: Tools to Enhance Your Coding

Today’s AI-powered tools are transforming the approach to coding, delivering faster solutions to complex problems. By addressing your team’s daily development needs with effective AI techniques, you can maximize productivity without sacrificing code quality and stay ahead of the competition.

We have released the AI Engineering Productivity Cookbook to help engineers seamlessly integrate AI coding tools into their projects. The Intellias Technology Office designed this guide for engineering, product, and innovation teams based on the Intellias AI Copilot Implementation program. In a recent Intellias webinar, Roman Kyryliuk, Senior .NET Engineer at Intellias, introduced the research and shared his experience, giving valuable insights into using GitHub Copilot and other AI tools. Let’s explore how AI implementation can impact your development processes, optimize workflows, and drive innovation. 

Which AI coding tool to choose 

AI tools can assist you in almost everything — from automating routine tasks to providing contextually relevant code suggestions, saving hours of development time. We have selected the three most popular stacks to do similar code completion tests and measure what AI tool is the best for coding on the market. So, here’s a brief comparison of top AI-powered coding assistants:

• GitHub Copilot: Rated the most powerful, offering the best real-time code suggestions. It stands out for its ability to handle complex requests and generate accurate models and logic based on different inputs like JSON, CSV data, etc.
• AWS CodeWhisperer: Effective but not as reliable in generating specific code outputs or handling external APIs compared to GitHub Copilot.
• Tabnine: Good for specific tasks but less efficient for backend and large-scale applications.

We tested these tools with popular programming languages like .NET, Java, and JavaScript. GitHub Copilot consistently outperformed the others in generating accurate, production-ready code with minimal errors. 

When starting to use AI tools for development, it’s important to understand that results may not always be ideal on the first try, and that’s perfectly okay. The key is to invest time in learning how the tools work and how to interact with them effectively. Initially, developers might spend more time getting accurate results, but this will significantly reduce with practice. 

The learning curve involves understanding how to craft precise prompts from the very beginning to spend less time adjusting queries and more time focusing on core development tasks. Even if early attempts seem off-track, persistence and refining your approach will eventually lead to success. 

How AI tools can ease code development 

AI can assist you in the various development tasks:

Model generation. GitHub Copilot can generate complex models from simple CSV or JSON, XML, and other inputs, dramatically speeding up workflows.

External API integration. Copilot also excels in accessing external APIs and generating code that interacts with real-time data, such as currency conversions, using a live API.

Frontend development. Even for frontend developers, AI tools like GitHub Copilot can assist in generating components and automating repetitive UI tasks in frameworks like Vue.js, Angular, or React.

Automated unit testing. Writing unit tests, which can be tedious, becomes more accessible with AI tools. For example, GitHub Copilot was able to generate useful test cases, though developers may still need to refine the output. 

Pros and cons of AI-assisted coding

Benefits

• Elevated productivity. AI tools reduce the time spent on repetitive coding tasks, enabling engineers to focus on more complex problem-solving.
• Bug reduction. By offering best practices and accurate code snippets, AI tools help minimize logic errors.
• Faster learning. AI tools expose developers to the latest libraries, frameworks, and methods, making it easier to stay up to date.
• Real-time documentation. Tools like GitHub Copilot even provide links to relevant documentation, helping engineers learn on the go.

Challenges

• Skill atrophy. Relying too heavily on AI can lead to a lack of deep understanding. Over time, this could diminish a developer’s ability to write complex solutions or grasp intricate domain-specific problems.
• Shallow problem-solving. As developers become more reliant on AI-generated code, there’s a risk of losing the ability to devise unique solutions, relying too much on what AI suggests.

Future of AI Coding  

The future of AI coding looks promising as AI tools continue to evolve rapidly, enhancing developers’ productivity and capabilities. Tools like GitHub Copilot are improving daily, offering features that weren’t available even the day before. To stay competitive, developers must remain updated with AI advancements and learn how to balance their coding skills with AI assistance. 

In the future, strong developers will need technical expertise and proficiency in leveraging AI tools efficiently. As AI tools become increasingly powerful, the ability to use them in conjunction with human oversight will be critical for maintaining high-quality code, thus reshaping the expectations and speed of modern software development.

Download the AI Engineering Productivity Cookbook for free here.