As a junior programmer always aiming to enhance my efficiency and understanding, I’ve recently discovered the benefits of Cursor’s AI-powered tab completion. In this post, I’ll share my experiences integrating Cursor into my workflow, specifically when writing unit tests and repetitive code segments. I’ll outline both the strengths and limitations I’ve encountered and discuss the areas I plan to explore next, such as providing Cursor with additional context from tools like Jira.
Using Cursor Tab Completion
My workflow frequently involves using Cursor’s tab completion to streamline unit testing and repetitive coding tasks. When working on a feature, I typically, run relevant tests in my terminal with hot reload, write descriptive test function names, and organize tests clearly into Arrange, Act, and Assert sections. These practices help narrow Cursor’s focus and greatly enhance the quality and relevance of its suggestions. Once these steps are complete, I activate Cursor Tab and carefully review its suggestions to ensure they align with my needs.
Example workflow for narrowing Cursor’s focus by giving structure and context.
In this workflow, Cursor excels at identifying and applying established test patterns within the codebase, often accurately suggesting necessary steps for thorough testing. This significantly reduces manual typing, boosts my productivity, and allows me more time to create new logic and patterns. That’s particularly beneficial at my stage as a junior developer.
Additionally, I’ve become familiar with useful Cursor keybindings, such as Shift + Command + P for quickly accessing the command palette. This palette is helpful for toggling Cursor Tab on (Enable Cursor Tab) and off (Disable Cursor Tab). Because Cursor Tab isn’t always beneficial, easily toggling it ensures flexibility and helps maintain my focus.
Example of using the command palette to disable Cursor Tab.
The Pros of Cursor Tab Completion
One major advantage of Cursor is its capability to significantly reduce repetitive coding tasks, freeing up valuable time and cognitive resources for tackling more complex problems. Beyond code generation, Cursor Tab offers additional helpful features. Cursor’s Smart Rewrites efficiently corrects minor syntax mistakes, such as missing commas or misplaced curly brackets, saving me valuable debugging time. Cursor Prediction further boosts efficiency by anticipating the next logical step in my coding, quickly moving me exactly where I need to be with just a tab press. This helps reduce decision fatigue and accelerates my workflow. Overall, Cursor allows me to edit and iterate on code much faster.
Example of Cursor’s Smart Rewrites feature.
The Cons of AI Tab Completion
Despite its strengths, Cursor Tab also has limitations. Occasionally, it provides overly generic or irrelevant suggestions, disrupting my workflow. Which is why knowing when an AI tab completion is useful — and when it isn’t — is essential. I have not found Cursor Tab useful for creating new production-level code or patterns. Almost always with new functionality, there is editing on my part to either bring the code up to team standards or even fix the logic based on the story’s needs.
Furthermore, over-reliance on AI completions can also limit exploration of alternative solutions or deeper conceptual understanding, a critical risk for junior developers like me who need continuous growth and learning. To mitigate this, I make sure to fully understand all code I commit to the repository and regularly explore unfamiliar code. I have found using Cursor Tab as a text editing tool rather than a pair programmer to be particularly helpful in maintaining steady professional development and productivity.
Overall, Cursor tab completion has positively impacted my development workflow, increasing productivity by simplifying and decreasing the cost of routine tasks. Moving forward, I plan to experiment with providing Cursor additional context from Jira stories, believing that expanded context on the feature I am working on could improve suggestion accuracy and relevance. I’m also interested in exploring Cursor Rules to define team-specific patterns to further enhance suggestion quality. As Cursor continues to evolve, its improved contextual understanding promises to enhance productivity further.