Coding with Assisted Generative AI: Transforming Automation

Why Structured AI Output Reduces Time, Errors, and Rework

In the world of automation, delivery speed is often limited by how quickly we can turn our vision into code. SenseTalk scripting from Keysight's Eggplant is powerful and flexible. However, like many domain-specific languages, it requires discipline. We need to apply standards consistently, ensure reliable error handling, and document every handler for reuse. When we do these steps manually, it takes time and can lead to inconsistencies.

To tackle these challenges, we adopted generative AI. Our goal was never to replace engineers but to empower them to work faster and smarter. By guiding AI models with well-defined standards and guardrails, we can produce structured, consistent code ready for use. This shift allows engineers to focus on test logic and automation strategy while the repetitive scaffolding is generated for them.

Manual Coding vs. AI-Assisted Coding

Writing a handler manually can take anywhere from twenty minutes for simple tasks to over two hours for complex cases. Each step involves recalling standards, formatting comments, adding logging, and ensuring proper control flow. With AI assistance, we can generate the same handler in minutes—often in seconds for simpler tasks. While review and integration still occur, the time savings are substantial.

But speed isn't the only benefit. Consistency is crucial. AI produces handlers that look and behave the same across projects. This uniformity reduces rework, shortens review cycles, and improves long-term maintainability. Engineers no longer reinvent the wheel, and new team members can contribute more quickly because the codebase is cohesive.

Online vs. Offline Models

We utilize both online and offline AI models, each serving a unique purpose. Online models offer speed, context retention, and advanced reasoning. They can anticipate missing details and often generate handlers that are correct on the first attempt. On the other hand, offline models are slower and require more explicit prompting, but they provide security by ensuring that data never leaves the environment.

Both options outperform manual coding. Online models speed up development and reduce iteration time. Offline models enforce discipline and consistency, especially in highly regulated or isolated environments. The choice between speed and security depends on the specific needs, but in either case, the outcome is superior to human-only coding.

Why Offline AI Still Matters

Even when speed is not a priority, offline AI provides significant value. Handlers generated offline are parameterized, reusable, and well-documented. They may take longer to refine, but they create a consistent framework that reduces long-term maintenance costs. In environments where security and compliance are paramount, this model allows teams to benefit from AI-assisted coding without compromising sensitive data.

Quality Improvements

AI assistance enhances quality by embedding standards directly into the code. Each handler comes with documentation, usage examples, parameter descriptions, and notes. Logging is consistent, error handling is explicit, and parameterization prevents the drift that often occurs with hardcoded values. The result? A library of reusable building blocks instead of a collection of one-off scripts.

The Impact of Generative AI on Development

Generative AI accelerates development, reduces errors, and enforces standards in ways that manual coding simply can't match. Even offline AI delivers measurable ROI by producing consistent, reusable, and documented code. Tracking metrics such as time saved, error reduction, and rework avoided is crucial for demonstrating value. Revisiting past AI code sessions also allows us to repurpose work for new projects, extending the benefits over time.

The Future of Automation

The future of automation isn't about choosing between humans and AI. It's about structuring a partnership where humans focus on high-level strategy while AI provides reliable scaffolding. This approach boosts velocity, enhances quality, and ensures that automation remains sustainable and scalable across the organization.

In conclusion, the integration of AI into our coding processes is a game changer. It not only streamlines our workflows but also enhances the quality of our outputs. If you want to dive deeper into this exciting methodology, check out our webinar with Keysight: Click here for a YouTube video!

Let's embrace the future of automation together!