I'll be honest—I'm a bit lazy, not in a bad way, but in the "how can I get more done with less effort?" way. As a web engineer, I'm always looking for ways to improve my workflows to spend less time on repetitive tasks and more on creative problem-solving. AI has become my weapon of choice, helping me build software faster and smarter than ever before. One of my favourite tools in this space is Cursor, an AI-powered code editor that has completely changed how I develop software.

Using Cursor to Build Software

Whether writing boilerplate code, debugging issues, or generating complex logic, Cursor helps me accelerate my workflow. The seamless integration of AI with my IDE allows me to:

• Generate functions and components quickly
• Get instant code suggestions and improvements
• Debug and troubleshoot problems efficiently
• Automate repetitive coding tasks

Thanks to this assistance, I can focus more on high-level architecture and problem-solving rather than getting bogged down by syntax and routine coding patterns. The best part? It's incredibly easy to get started. Just install it, integrate it into your workflow, and watch it take over the tedious parts of coding. But while anyone can use Cursor, I've found ways to use it better—ways beyond just auto-completing code. Stick around, and I'll share how I get the absolute most out of it.

Be Smart About Prompting

One of the most crucial aspects of working with AI tools is crafting effective prompts. The quality of the output is directly related to the quality of the input. Context is king—providing clear, well-structured instructions helps AI models generate meaningful and useful responses.

Here are some key principles to keep in mind when prompting AI:

• Be specific: Vague prompts lead to generic responses. Define what you need in detail.
• Provide context: The more relevant information you give, the better the AI understands your request.
• Iterate and refine: If the first response isn't quite right, tweak your prompt and try again.
• Use examples: Providing examples or expected formats can help guide AI-generated content.
• Break down complex queries: If your request is complex, breaking it into smaller steps can improve accuracy.

By mastering prompt engineering, engineers can unlock AI's full potential, making it a highly effective assistant rather than just a tool. However, while prompting can generate impressive results, it also comes with its own set of challenges.

Treat Your Agent Like an Intern

One of these challenges is making sure AI-generated code adheres to your team's or organisation's coding standards and practices. Just like an intern or junior engineer, your AI assistant needs guidance to produce high-quality work. While AI can generate impressive results, it doesn't inherently know the best coding practices your team follows. That's why I leverage Cursor Rules to ensure the AI's output aligns with the coding standards we agreed on as a team.

Cursor Rules act as safeguards, enforcing best practices and preventing AI from generating inconsistent or subpar code. These rules include:

• Formatting standards: Ensuring consistent indentation, naming conventions, and style guidelines.
• Code quality checks: Avoiding anti-patterns, enforcing modularity, and ensuring proper documentation.
• Performance considerations: Encouraging efficient algorithms and minimising unnecessary computations.
• Security practices: Ensuring safe handling of data, authentication flows, and API interactions.

Essentially, these are the classic Boy Scout rules—always leave the code better than you found it. Just like when we mentor interns and junior engineers, we need to train our AI assistants to adhere to best practices.

Getting Structured Output in Large-Scale Projects

Another major hurdle is consistency of output—due to the inherent stochastic nature of large language models, if you ask an AI to solve the same problem multiple times, you'll often get different implementations. This can lead to fragmented code, redundant logic, and a lack of cohesion across your project. This might not be a big deal in small scripts, but in larger projects where multiple people work on the same code, maintaining a structured and scalable architecture requires more than just good prompts.

To address this, I use Nx Generators —a tool that automates the creation of components, modules, and configurations based on predefined templates—to establish scaffolding structures that guide my Cursor agent. Nx Generators provide a consistent framework for AI-generated code, ensuring:

• Standardised implementations: Combined with Cursor rules, we can instruct the agent to use specific generators when performing certain actions, reducing inconsistencies.
• Codebase uniformity: Every component, service, or feature is generated with the same patterns, making collaboration easier.
• Scalability: Large projects remain maintainable because AI adheres to an organised framework.

Generators are just a small part of what Nx offers. It's a powerful tool for managing large-scale projects, enforcing modularity, optimising builds, and streamlining workflows across multiple teams. Using Nx, I ensure that AI-generated code doesn't just fit into the project—it enhances the overall architecture and development experience.

Introduction to Model Context Protocol (MCP)

MCP, an open protocol developed and pioneered by Anthropic, is the sauce that helps AI understand and interact with the tools engineers use every day. It acts as a bridge between a large language model (LLM) and external services, like codebases, issue trackers, and deployment pipelines. Or as they describe it: "Think of MCP like a USB-C port for AI applications. Just as USB-C provides a standardised way to connect your devices to various peripherals and accessories, MCP provides a standardised way to connect AI models to different data sources and tools."

MCP is designed to:

• Provide structured context to AI models, improving response accuracy.
• Facilitate interactions with external systems, reducing manual overhead.
• Enhance automation by leveraging AI-driven commands within development workflows.
• By leveraging MCP, engineers can create more intelligent and efficient integrations between AI tools and their software development lifecycle.

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Building a Custom MCP Server for Azure DevOps

AI-assisted coding has already significantly improved my development speed. Recently, I took automation one step further by building a custom MCP server to interface with Azure DevOps directly from within Cursor chat.

This integration allows me to interact with Azure DevOps seamlessly without leaving my development environment. Here's how it works:

• Fetching work items: I can retrieve work items, tasks, and issues from Azure DevOps using AI-generated queries.
• Accessing acceptance criteria: By accessing work items, I can directly reference acceptance criteria, allowing me to create precise prompts and generate test cases based on project requirements.
• Tracking "side quests" in real-time: If I come across new tasks while coding, I can ask my agent directly to create a new work item, ensuring I can pick it up later without losing context.
• Better contextual awareness: AI agents can better understand the current codebase by referencing work items and related repositories within the project.

By integrating Azure DevOps with Cursor, I've significantly reduced context-switching and manual interactions, making my workflow far more efficient.

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The Future of AI-Driven Development

AI-powered coding assistants are revolutionising software development, and as a developer at a digital product agency, I'm witnessing this transformation firsthand. Tools like Cursor aren't just improving our workflow—they're fundamentally changing how we deliver value to our clients.

Working in an agency environment demands both speed and excellence. Every project comes with tight deadlines and high expectations, making AI integration not just beneficial but essential. My custom MCP for Azure DevOps exemplifies how AI can streamline our development process, but it's just the beginning of what's possible. Every hour saved in development translates directly to better results and happier clients.

Here are three key advantages that AI-powered development delivers:

• Faster results: Our AI tools handle both basic and complex tasks automatically, helping us build software more quickly. This means our clients can test new ideas and launch products before their competitors do.
• Reliable quality: We use AI to check our work and maintain high standards, even when deadlines are tight. This consistent quality helps us build trust with our clients and keep them coming back.
• More room for fresh ideas: When AI takes care of the everyday tasks, our team can spend more time creating new and exciting features.

The pace of AI advancement in development is relentless. What's cutting-edge today might be standard practice tomorrow. But that's exactly why embracing these tools now is crucial. At In The Pocket, we've found that AI integration isn't just about keeping up—it's about staying ahead and delivering exceptional value to our clients.

The future of software engineering is clear: those who use AI tools in a smart way and tailored to the specific (client) need will be industry leaders, while those who hesitate risk falling behind. Regardless if you’re an engineer, an agency, or a company, the question isn't whether to adopt AI, but how quickly and purposely you can integrate it into your workflow.

Want to learn exactly where and how AI can transform your business? Reach out to our team to explore how to integrate AI in your organisation or build AI products that matter.

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