Hey everyone! 👋
I’ve been experimenting with ways to make Cursor’s suggestions feel more *personalized* to my team’s style and our codebase’s quirks. The biggest win so far? Creating a small, high-quality "golden dataset" of our own code to guide it. It’s not about dumping our entire repo, but curating examples of patterns we want to reinforce.
Here’s a simple workflow I’ve been using:
* **Define Your "Golden" Criteria:** What makes code good in your project? For us, it’s specific error handling patterns, our API response format, and our preferred way of structuring React components.
* **Gather Examples Manually:** I went through our recent commits and picked 10-15 files that perfectly embody these patterns. Think of them as your coding style’s "greatest hits."
* **Create a Dedicated Project Directory:** I made a `.cursor/examples` folder in my project root and placed these curated files there. You can also use a separate, small reference repo that you open alongside your main project.
* **Provide Context in Prompts:** Now, when I ask Cursor to generate something new, I can say things like, *"Similar to the error handling in `api_utils.js` in our examples, can you..."*
The key is quality over quantity. Feeding it a few pristine examples of *how we actually write* has been way more effective than hoping it infers style from a giant, messy codebase. It’s cut down my review time for AI-generated code significantly.
Has anyone else tried a similar approach? I’m curious if you’ve found other tricks for "teaching" Cursor your patterns, or if you use a different structure for your reference files.
– Amanda
Show me the accuracy numbers.
This is an excellent, pragmatic approach to what's essentially creating a few-shot prompt template for your specific codebase. It directly leverages the in-context learning capabilities of the underlying model without requiring fine-tuning.
One critical caveat from an experimentation standpoint: the effectiveness of this method hinges heavily on the retrieval mechanism. When you reference a file in your `.cursor/examples` directory, how is that context being selected and inserted into the prompt? Is it the entire file, or just relevant snippets? The token window is a finite resource, and stuffing it with entire files could push out other necessary context.
You might consider adding a step to distill your "golden" files into focused, annotated examples. For instance, instead of a full 200-line component, you could extract just the error-boundary lifecycle methods with a comment explaining the pattern. This increases the signal-to-noise ratio for the model.
The statistical analogy here is providing a strongly informative prior through your curated examples, which should steer the model's output distribution toward your team's preferred patterns.
Nullius in verba
That's a great point about token space. If you're pulling whole files, you're probably wasting most of it. But if you're manually trimming examples down to just the relevant snippets, doesn't that add a lot of maintenance overhead every time the "golden" pattern evolves a bit?
Is there a tool you'd recommend for automatically extracting just the parts of a file that match certain patterns? Like, could you tag lines with special comments and have a script pull those blocks?
Containers are magic, but I want to know how the magic works.