If you’ve ever kicked off a software project and immediately felt underwater—trying to make sense of scattered docs, research notes, stakeholder input, and legacy decisions—you’re not alone. As a maker, understanding the full landscape is essential. But getting there? It’s often messy and time-consuming. Recently, I started using Cursor—an AI-native code editor—to build a living, searchable knowledge base for my projects. It’s made a noticeable difference in how quickly I can get my arms around complex information and start producing real design deliverables.
This post is a look at how I’m using Cursor to cut through the noise—and why I think this approach will be a game-changer for software teams.
The Problem: Information Everywhere, Insight Nowhere
Software projects generate a lot of content: user research, stakeholder interviews, feature requests, architecture diagrams, bug reports, internal Slack threads—you name it. Each artifact tells part of the story, but they rarely live in one place. So when it’s time to scope a new feature or map out a workflow, I’d often spend hours digging through folders, pinging teammates, or rereading old docs to get the full picture. Even then, important details could slip through the cracks.
It wasn’t just inefficient—it was mentally exhausting.
The Setup: Defining Requirements Without the Overhead
That’s where Cursor comes in. At first glance, it’s a code editor with built-in AI. But I’ve started using it for something much broader: as a centralized, AI-assisted hub for defining system requirements and documenting product strategy.
Here’s how it works.
To start, I feed it everything—research notes, transcripts, project briefs, meeting recaps, feature specs.
I use natural language to interrogate that information:
- “This feature is too complex for the first release. How can we simplify it without boxing ourselves in?”
- “Help me refine our personas based on what we’ve learned from the stakeholder interviews.”
- “Reference these files to produce a step-by-step user flow.”
It helps me distill scattered input into structured outputs, such as personas, requirements, user flows, and edge cases.
This lets me operate almost like a product owner—surfacing what matters, aligning priorities, and documenting logic—without spending days piecing it all together manually. And because that foundation is clear, I can shift more of my mental energy toward design artifacts: clickable prototypes, UI flows, and presentations that move the project forward.
In short: better input, faster output.
The Impact: More Clarity, Less Waste
Since I started building my knowledge base this way, a few things have changed:
- I ramp up faster. When I join a project or revisit one after a break, I can quickly reorient myself by skimming AI-generated summaries or asking for a quick refresher.
- I make better decisions. With a clear view of past decisions and rationale, I’m less likely to duplicate work or chase dead ends.
- I work more efficiently. Turning knowledge into design artifacts (like flows, briefs, or prototypes) feels less like starting from scratch and more like building on solid ground.
Put simply, it helps me move from chaos to clarity faster.
Stop Managing Docs—Start Using Them
If you’re still jumping between different applications, pasting notes into scattered documents, or mentally stitching together context from five places, there’s a better way.
Feed everything into an AI-native tool like Cursor. Not just one doc—all of it. Research, strategy docs, meeting notes, specs. Let the system help you synthesize and reference it all in one place.
This isn’t just about summarizing key takeaways. It’s about analyzing and operationalizing project knowledge that would otherwise stay scattered and disconnected. Cursor can reference anything you load into it, so you don’t have to remember every detail—it’s all searchable, contextual, and ready to work for you.
The result? You spend less time wrangling information and more time building great software.