AI Coding Isn't About Better Prompts—It's About Better Process
Cursor and similar AI coding tools are genuinely transformative. They can understand context, generate substantial code blocks, and reason through complex implementation details in ways that feel like pair programming with an expert.
But there's a gap between what these tools can do and how most developers use them - that's why we open-sourced Cursory.
The Real Challenge with AI Development
The problem isn't that AI coding tools are limited—it's that they're too capable. When you can generate a full feature in minutes, traditional development guardrails break down.
Watch an experienced developer use Cursor without structure. They'll start with a vague idea, let the AI suggest an approach, iterate on that approach, and two hours later find themselves with working code that they don't fully understand or trust.
The AI did exactly what it was supposed to do. But the development process fell apart.
Why Process Matters More in AI Development
AI coding tools amplify your development velocity, but velocity without direction is just chaos. When AI can implement your ideas faster than you can think them through, the traditional checkpoints that keep development organized disappear.
That's why we built cursory—not to constrain AI capabilities, but to ensure that human expertise guides AI execution.
The Principles That Scale
1. Intentional Architecture
Every project starts with a Product Requirements Document (PRD) that defines not just what you're building, but why. This forces architectural thinking before implementation begins.
AI is incredible at implementation. But it can't make product decisions for you, evaluate tradeoffs like performance vs. maintainability, or understand which technical choices align with your team's operational constraints.
2. Early De-risking Through Discovery
The cursory process includes a Discovery phase that explicitly identifies unvalidated assumptions, technical feasibility questions, API limitations, and integration unknowns. Things that seem obvious in planning but become blockers in implementation.
AI can reason through complex problems within known constraints, but it can't anticipate domain-specific edge cases, infrastructure limitations, or third-party service quirks that only surface during integration.
3. Atomic Task Definition
Break work into self-contained chunks with explicit acceptance criteria and clear definition of done. Each task should be completable in a single focused session with minimal context switching.
AI excels when given well-bounded problems with clear success metrics. Ambiguous scope leads to meandering implementations that solve the wrong problem efficiently.
4. Observable Development History
Conventional commits, comprehensive test coverage, and architectural decision records create a queryable history of your system's evolution. When debugging production issues or onboarding new team members, this context is invaluable.
Your commit history should read like a technical narrative: what changed, why it changed, and what alternatives were considered. "feat: implement circuit breaker pattern for external API calls" tells a story. "fix stuff" doesn't.
The Real Benefit: Confidence at Scale
Here's what changes when you combine AI capabilities with structured process:
Faster debugging: When something breaks, the task breakdown and commit history tell you exactly what changed and why
Better code reviews: Architecture decisions are explicit and documented, making it easier for teammates to understand and critique changes
Reduced technical debt: Large refactors become manageable because the codebase structure was planned, not emergent
Team scalability: New developers can contribute quickly because patterns and decisions are consistent and documented
It's About Human-AI Collaboration
The best AI development isn't about writing better prompts or finding clever tricks. It's about creating a partnership where human judgment guides AI execution.
AI handles the implementation details you'd rather not think about. You handle the product vision, architectural decisions, and quality standards that require human expertise.
Getting Started
The cursory repo includes six rule files that you can integrate into any Cursor project:
new-project-setup: Project initialization guidelinesworkflow: The overall development processtech-architecture: Technology choice frameworkstask-structure: How to break down and complete workconventional-commits: Commit message standardsrule-creation: How to evolve the process
These aren't constraints on AI—they're frameworks that help AI understand your intentions and standards.
The Future of Development
AI coding tools will only get more capable. The developers who succeed won't be those who learn to prompt better, but those who learn to collaborate better—maintaining human oversight while leveraging AI acceleration.
Because the best AI development isn't about replacing human expertise. It's about scaling it.
Check out the cursory repo and turn AI capabilities into structured results.