How to Efficiently Use AI Coding Tools to Speed Up Software Development

Getting Started with AI Coding Tools

As companies start shifting towards AI, so should developers, not only do these tool improve speed and throughput, they also can enhance code quality, reduce repetitive tasks, and act as intelligent collaborators. Developers who embrace AI powered tools gain a competitive edge by automating mundane coding tasks and improving overall productivity. AI coding tools are transforming how developers work. No longer do we have to spend endless hours battling syntax errors or trying to understand complex code. Coding assistants help developers become more creative and productive by offering intelligent, context aware suggestions.

I can recall a particularly stressful sprint when my team faced a codebase that had become increasingly complicated. It felt like we were bogged down by the past instead of moving forward. This challenge led me to explore various AI tools, and the difference was astounding. Our workload became lighter, and I discovered new efficiencies.

By the time you finish this guide, you will know how to successfully integrate these AI tools into your workflow, streamline your development, and boost your productivity.

Overview of AI Coding Tools

AI coding tools have become essential for modern programmers. They offer much more than just simple code suggestions. Here are the tools I use on a daily basis I will show how I utilize these tools and my best practices.

• Cline: This AI-driven tool provides personalized code completion along with language-specific patterns to help you write code faster.

  
  

• Aider: Aider merges chat-like interactions with inline suggestions, allowing you to code and debug in a collaborative way.

  
  

• Cursor: Cursor focuses on enhancing workflow efficiency by delivering contextual suggestions that fit the current project requirements.

  
  

• llm (Language Model): This robust tool harnesses large language models to create and understand complex code snippets, making it ideal for large projects.

  
  

Each tool has its own benefit, like choosing the right tool for the job you wouldn’t use a hammer when a nail gun can be more efficient option. Similarly, Cline excels at structured code planning, Aider is perfect for interactive debugging, Cursor streamlines workflow with in-editor AI assistance, and llm enables precise prompt engineering. By leveraging the right tool in the right context, developers can maximize efficiency, reduce friction, and write cleaner, more maintainable code.

When I first tried Aider, its ability to offer inline documentation while I was coding struck me. It made my workflow much smoother and more efficient.

Best Practices for Different Project Sizes

Developers often face various project sizes, each requiring different approaches to effectively use AI coding tools. Here is how to adapt these tools based on your project needs.

Small Repositories

In smaller repositories, AI coding tools can offer quick benefits with minimal setup. Due to the smaller codebase, suggestions are usually more focused and relevant.

For example, if a developer is working on a personal project and needs to add a feature quickly, using Cline can significantly reduce coding time. One developer noted that they were able to implement a new function in just 30 minutes using intelligent suggestions from Cline, instead of the usual 90 minutes spent wrestling with the syntax.

However, it’s essential to find a balance between AI assistance and personal creativity. Over-relying on AI can sometimes stifle innovative thinking.

Large Repositories

Larger repositories come with their own sets of challenges and opportunities. As these codebases grow, they can become complicated, which means developers need a more structured use of AI tools. As the code base grows, so does the context, and as context increases, the performance of AI tools diminishes at an exponential rate. This is where you will begin to notice AI hallucinations. What is the solution? Utilizing their strengths, each has its own strengths and weaknesses. We should understand what they are, how to utilize strengths, and avoid the pitfalls.

Each tool has its own benefit, like choosing the right tool for the job you wouldn’t use a hammer when a nail gun is the better option. Similarly, Cline excels at structured code planning, Aider is perfect for interactive debugging, Cursor streamlines workflow with in-editor AI assistance, and llm enables precise prompt engineering. By leveraging the right tool in the right context, developers can maximize efficiency, reduce friction, and write cleaner, more maintainable code.

Best Practices for Each Tool

Cline

I found great success by adding a rules file that contains all the project specific requirements. here is a link to an example file I created below. Once the file is created based on your project you should setup the Custom Instructions, credit due: Reddit post.

Custom Instructions:

You must strictly adhere to the rules and guidelines outlined in the AI_RULES_AND_GUIDELINES.md file for the [ADD YOUR PROJECT NAME] project. Before responding to any request, verify and update the memlog files. Break down tasks into clear, numbered steps with explanations. Follow all coding, security, and documentation practices specified in the guidelines. If any action contradicts these rules, flag it immediately. Your responses should demonstrate compliance with these guidelines, and you should actively reference them when providing assistance or generating code. Failure to follow these rules is not an option.

The prompts I tend to stick to are task-based, I ask cline to create me a clear tasks list that it can work off, once it accomplishes the task we mark the task as complete. If I want to add a new feature, I would prompt:

First Prompt (Planning the work):

Please review the project structure and technical architecture outlined in example_project_summary.md. Based on this, create a detailed plan for implementing the core features of the log-aggregator application.

Second Prompt (doing the work):

Can you start the task: 2. Data Processing Pipeline Enhancement. Note: I am noticing I am missing some test cases can you add it to our tasks list and ensure when you are building task 2 to add tests for code coverage of 90%. please run the tests to ensure they are passing as you create them.

Examples of my task files: https://github.com/travism26/Security-Monitoring-Suite/tree/main/memlog

Since Cline's strengths are based around task-oriented workloads, you should instruct Cline to create task lists, and then instruct Cline to work off the tasks. This will help keep Cline on track and not veer off task and waste time and resources (tokens).

Cursor

Working with cursor is slightly different since the strengths to cursor are more around day to day development. I tend to use the chat feature more often then tab, I like asking design questions how something should be refactored or implemented based off X interface. Given that, I noticed that if cursor doesnt have the necessary context it tends to hallucinate or introduce new dependencies to your project. Here is an Example Cursor Rules File credit goes to a youtube channel that I found helpful.

Aider

This tool gives you more flexibility with context, model and the prompt, I like its architect mode. architect mode enables you to first instruct the model to first propose a solution to you before it starts coding. This is similar to the "Plan" mode in cline. I like the fact you can choose which model can architect and which model will be the writer. I encourage you to review the leader boards this has which combo has the highest accuracy. This tool has the openrouter option as well allowing you to utilize the openrouter api key (which I recommend). One youtube channel I highly recommend is Indydevdan he uses this idea of "Spec prompts" which are great to know.

LLM

This tool I mainly use to iron out my prompts in my codebase. Sometimes, testing prompt results returned from an LLM can be painful, especially when dealing with dynamically generated prompts. What I usually do is log the prompt and copy it from the logs into a file that the LLM will utilize. I then just pass the prompt into the LLM. There are some tricks on how to improve the prompts like using xml tags to give it clear structure and balancing the context with the prompt itself.

Embracing AI Coding Tools for Greater Efficiency

Integrating AI coding tools into your development routine opens up exciting avenues for enhanced productivity. Whether you're tackling a small project or a massive codebase, these tools can help automate routine tasks, provide insightful suggestions, and create a more collaborative environment that supports developers in achieving their goals.

By adopting AI coding tools like Cline, Aider, llm, and Cursor, you can change your programming approach for the better. The real advantage lies not just in simplifying coding tasks but in amplifying your creative potential and improving problem-solving skills.

If you've ever experienced the frustration of prolonged debugging sessions, consider trying out these technologies. With AI coding tools, your future code might just be a suggestion away.

A close-up of a programming environment showcasing code snippets.

A neat workspace filled with programming books and resources.

The path forward is clear: explore AI coding tools, apply best practices specific to your projects, and watch your productivity soar. Now is the time to take action! Start by integrating an AI coding tool into your workflow today, share your experiences with your team, and embrace the future of coding. Happy coding!