Our engineering team uses AI coding tools every day. Not as experiments — as production tools that generate real code for real clients. After 200+ production tasks across Cursor, GitHub Copilot, and Claude, we have strong opinions backed by data.
Here's what actually matters when you're shipping code, not writing demos.
The Test
Over three months, we tracked every AI-assisted task across 12 client projects. For each task, we recorded which tool was used, how much of the generated code survived review, what had to be rewritten, and why.
Our clients are mid-market companies in Southern California — SaaS platforms, healthcare tech, e-commerce, and fintech. The code is Python, TypeScript, and React, deployed to AWS and GCP.
Cursor: Best for Feature Development
Cursor's strength is its understanding of your entire codebase. When you're building a new feature that needs to integrate with existing code, Cursor consistently generated the most contextually accurate output.
For a healthcare client in Irvine, Cursor built a patient dashboard component that correctly referenced their existing data models, API patterns, and component library. The first draft was 80% production-ready — the remaining 20% was error handling and edge cases.
Where it struggles: Cursor sometimes over-indexes on existing patterns. If your codebase has technical debt, Cursor will replicate it faithfully. It also tends to generate overly complex solutions when a simpler approach exists.
Review pass rate: 72% of Cursor-generated code survived review without major changes.
Copilot: Best for Boilerplate and Tests
Copilot excels at repetitive, pattern-based code. API endpoints that follow a consistent structure, CRUD operations, test suites that cover standard cases — this is where Copilot saves the most time.
For an e-commerce client in El Segundo, Copilot generated 40 API endpoint tests in an afternoon. The patterns were consistent, the assertions were correct, and our reviewers only needed to add edge cases.
Where it struggles: Copilot generates plausible-looking code that's subtly wrong more often than the other tools. It's particularly bad at complex business logic and anything involving state management. It also frequently suggests deprecated APIs and outdated patterns.
Review pass rate: 61% of Copilot-generated code survived review without major changes.
Claude: Best for Complex Logic and Refactoring
Claude's advantage is reasoning. When the task requires understanding business rules, refactoring complex functions, or debugging subtle issues, Claude consistently outperforms.
For a fintech client in Costa Mesa, Claude refactored a 400-line payment processing function into a clean state machine pattern. It understood the business logic, preserved edge cases, and added error handling that the original code was missing. Our senior reviewer called it "better than what most mid-level engineers would produce."
Where it struggles: Claude doesn't see your full codebase by default, so it sometimes generates code that uses different naming conventions or patterns than your project. It also tends toward verbose solutions — correct but longer than necessary.
Review pass rate: 78% of Claude-generated code survived review without major changes.
Where All Three Fail
Security. Every tool generates code with security issues — hardcoded values, missing input validation, improper error messages that leak internal details, SQL queries built with string concatenation. None of them consistently think about authentication, authorization, or data sanitization.
Infrastructure awareness. AI doesn't know your deployment environment. It generates code that works locally but fails at scale — connection pooling, rate limiting, retry logic, circuit breakers. These are production concerns that require human experience.
Business context. AI can write the code, but it can't validate the requirements. We've seen AI tools confidently generate features that perfectly implement the wrong specification. A human who understands the business catches this in review. An automated pipeline ships it to customers.
Our Recommendation
Use all three. Cursor for feature work, Copilot for boilerplate, Claude for complex logic and architecture decisions. But never skip the human review.
The best AI coding workflow isn't about choosing a tool. It's about building a review process that catches what every tool misses.