How AI Makes Life Easier for Developers in the SDLC

We’re back with the SDLC Spotlight Series, offering a closer look at how GenAI can help developers handle complex software projects.  

Earlier, we discussed how GenAI can glean application knowledge from legacy codebases - here, how business analysts can get a lot more done in a lot less time – here, and how AI can be an ally to a Project manager - here.

As a developer, your day often involves juggling everything from planning and designing to coding and testing and refining the code you’ve written. It can get overwhelming, especially when repetitive tasks elbow out the creative problem solving on your schedule. Here’s where AI comes in—it simplifies workflows, abstracts away process complexities and lets you focus on business logic and scaling your app to a much higher number of users reliably & securely. 

Here are some use cases of AI in the Dev cycle: 

 1. Generating Reference Documents  

Designing a solution begins with creating High-Level Design (HLD) and Low-Level Design (LLD) documents—essential blueprints that outline the system’s structure and details. AI tools can revolutionize this process by analysing requirements and auto-generating initial drafts, providing a well-organized, early start. 

For developers, this means no more starting from scratch. AI tools deliver a framework with key components like system modules, workflows, and dependencies, saving valuable time and ensuring consistency across projects. Developers can then focus on refining and enhancing these drafts, rather than investing time in the foundational setup. 

AI tools go further by leveraging knowledge bases to reference best practices and past projects, ensuring consistency and adherence to standards. For HLDs, AI can outline the overall system architecture, propose modular designs, and create visual aids like flowcharts. For LLDs, it provides detailed logic flows, component-level interactions, and class diagrams. Importantly, these tools are not static; they integrate feedback dynamically, updating designs as requirements evolve. This ensures the documentation remains accurate and usable throughout the development lifecycle, making AI a critical partner in modern design workflows. 

While AI provides a strong head start, the final touches—those that make the design practical and efficient—rely on the developer’s understanding and experience. It’s the combination of AI’s speed and accuracy with a developer’s expertise that ensures the best outcomes in creating system designs. 

2. Aiding in Code Creation  

AI tools can come to the rescue of tired coders repeating the same boilerplate code. Based on your inputs, these tools auto-generate snippets or even complete sections, perfectly aligned with your specific requirements. Tools like GitHub Copilot, Cursor9, and ChatGPT are excellent examples of how AI can be embedded into your development environment to streamline this process.   

For instance, GitHub Copilot integrates directly into popular code editors like VS Code, suggesting lines of code or entire functions as you type. Cursor9 offers context-aware completions and real-time suggestions tailored to your project, helping with everything from simple boilerplate to complex data pipelines. Similarly, ChatGPT can be embedded as a conversational assistant, offering instant solutions, debugging tips, or even generating entire modules based on detailed prompts.   

By leveraging these tools, developers can speed up the development cycle, reduce errors in repetitive tasks, and focus their efforts on high-value activities like logic and architecture. AI handles the groundwork, enabling you to deliver robust and efficient code more effectively.

3. Reviewing and Refactoring Code  

Code reviews help developers maintain quality, but they are time-consuming. AI-tools simplify this by analyzing code in real-time, identifying bugs, inefficiencies, and vulnerabilities. They perform quality checks, flagging issues like inconsistent naming, redundant code, and suboptimal logic, while providing suggestions for improvement.   

AI excels at detecting gaps in exception handling, reducing runtime errors, and pinpointing performance bottlenecks. It suggests optimizations, such as refactoring loops or improving database queries, ensuring the code is both reliable and efficient.   

By catching issues early, AI helps developers maintain cleaner code and save time. While it handles repetitive tasks, developers focus on intricate logic and edge cases, ensuring the final code is robust, scalable, and ready for innovation. 

The Perfect Partnership Between Developers and AI 

AI helps a developer drive more value by automating repetitive tasks and time-consuming processes. This lets them focus on solving problems, creating innovative solutions, and improving their craft. 

From drafting design documents to speeding up coding and streamlining code reviews, AI makes the development process smoother and more efficient.  

If you’re a developer, having AI tools by your side can help you work smarter and achieve more. Want your brownfield project to hit milestones faster? Let’s talk! Email us at aiservices.business@prodapt.com to see GenAI in action.   

The journey through the SDLC continues. What new possibilities will GenAI unlock next? Stay tuned for more. 

Authored By – Lakshara Kempraj, AI Services Business team, Prodapt