The Future of Code: Applying GenAI to Code Creation

In the rapidly evolving landscape of enterprise software development, efficiency, scalability, and innovation are not just goals—they are requirements. As organizations maneuver increasingly complex systems and tighter delivery timelines, AI-assisted code generation is reshaping how software is conceived, built, and maintained. By automating repetitive tasks, enhancing code quality, and accelerating development cycles, AI-driven tools are not only streamlining workflows but also redefining the role of developers.

In this article, we will explore the intricacies of leveraging Large Language Models (LLMs) and Generative AI (GenAI) technologies to accelerate application development, focus on higher-value creative work, and produce enterprise-grade code with reduced effort.

The New Frontier of Human-Computer Interaction

AI/ML and LLMs have opened a new frontier in human-computer interaction, with GenAI enabling us to access, acquire, summarize, and create information faster than ever before. The potential of GenAI capabilities to produce greater efficiency and productivity spans industries and practices, from medical research to application building.

AI-assisted activities allow experienced professionals to abstract labor-intensive tasks that can be rapidly generated using an LLM, enabling them to focus on training and directing the LLM, and reviewing and refining outputs. Developing a software application is a complex process consisting of many layered steps and practices that can significantly benefit from the generative powers of LLMs and GenAI.

At Cadmus, we applied GenAI using Cursor with Claude as our primary platform to build starter UI code frameworks, develop service code scaffolding, assist with code reviews, develop test cases, and more. Through our experience, we put together the following 10 key considerations every team should keep in mind to effectively leverage AI-assisted development, ensuring it adds value while maintaining control, clarity, and security throughout the software lifecycle.

1. 1. Precise Prompting: Be exact in your prompts. For example, when we requested code to pull best practices from “a” GitHub repo while thinking of multiple repos, Claude generated code for a single repo integration. Our Prompt Template Library proved invaluable here, as templates underwent multiple iterations to eliminate ambiguities and edge cases. By maintaining a library of precisely worded prompts that had proven successful, we reduced misinterpretations and ensured consistent outputs across similar tasks. When a prompt produced unexpected results, we refined it and added the improved version to our template collection.

1. 2. Detailed Instructions: Don’t be afraid to be as detailed as possible in your prompts to ensure the AI understands your exact requirements. Our most successful template prompts often exceeded 500 words, containing specific formatting requirements, error handling expectations, naming conventions, and architectural patterns. The Prompt Template Library allowed us to reuse these detailed instructions without having to recreate them for each interaction. For complex components, our templates included examples of desired outputs alongside explanations of why certain patterns were preferred, giving Claude multiple ways to understand our requirements.

1. 3. Iterative Refinement: Don’t hesitate to modify instructions based on the results of the code generated by the agent. This is where your experience as a software engineer adds significant value.

1. 4. Active Participation: Don’t leave all decision-making to the AI agent. Be an active participant in the development process.

1. 5. Collaborative Problem-Solving: Suggest better ways to implement functionality or engage in conversation to identify optimal approaches.

1. 6. Code Quality Oversight: As software engineers, you need to detect “code smells” and address them in conversation with the agent.

1. 7. Strategic Token Usage: Don’t use the agent to fix runtime errors that you can easily address yourself. This consumes precious tokens and can be inefficient.

1. 8. Request Alternative Solutions: For critical functionality, always ask the agent for alternative implementation approaches, then decide what makes the most sense for your specific context.

1. 9. Division of Labor: Use the AI agent for heavy lifting of writing mundane code while maintaining control over the creative and architectural aspects of the solution.

1. 10. Collaborative Mindset: The best outcomes emerge when development becomes a collaborative session between human and AI. Every session should feel like wielding a brush to create a beautiful canvas—a sense of accomplishment where you feel you created the app with AI assistance.

Conclusion

AI-assisted code generation represents a paradigm shift in software development. Rather than replacing developers, it elevates their role to that of creative directors who can realize their visions more rapidly and comprehensively than ever before. The most successful implementations will be those that recognize the symbiotic relationship between human creativity and AI capabilities.

By following the best practices outlined in this article, development teams can harness the power of AI to dramatically accelerate development cycles, reduce costs, and produce higher-quality code while freeing human developers to focus on the creative and architectural aspects of software development where they add the most value.

The future of code belongs to those who can effectively collaborate with AI—speaking its language, understanding its strengths and limitations, and directing its capabilities toward realizing human creative vision.

To learn more about Cadmus’ journey developing an AI-powered application generator, download the expanded white paper below.