[SAMPLE] How We Use Cursor and Claude for AI-Assisted Software Development.

How our team adopted Cursor and Claude as daily drivers for coding, code review, and documentation—without losing quality or velocity.

The shift to AI-assisted development

Our engineering team started experimenting with AI coding tools in 2024. We wanted to reduce boilerplate, speed up debugging, and keep documentation in sync with the codebase—without sacrificing code quality or security.

Why Cursor and Claude? We evaluated several assistants. Cursor gave us a first-class editor experience with inline edits, terminal awareness, and project context. Claude’s long context and careful reasoning matched our need for reliable refactors and clear explanations. Together they became our default “pair programmer” for greenfield features and legacy maintenance.

How we use them day to day

Writing new code. For new APIs or UI components, we start with a short spec in a comment or doc. We ask Cursor to generate a first draft: types, tests, and error handling. The model sees the rest of the repo, so it reuses our patterns and naming. We always review and often trim or harden the output; the gain is in structure and consistency, not blind acceptance.

Refactoring and migrations. Moving to a new library or changing a shared interface is where Claude shines. We paste the relevant files and ask for a stepwise plan, then apply changes in small commits. Having the plan in natural language makes code review and rollback much easier.

Documentation and ADRs. We keep architecture decision records and runbooks in the repo. When we change behavior, we ask the assistant to suggest updates to the affected docs. That keeps “why we did it this way” aligned with “what the code does.”

Guardrails we keep

We never ship without human review. All PRs go through the same review checklist; AI-generated code is no exception. We also run static analysis and tests in CI. We treat the assistant as a fast, knowledgeable junior pair—not an autopilot.

Security and secrets. We never put secrets, API keys, or PII into chat. We use local or sandboxed contexts for sensitive code and rely on existing secret management.

Outcomes

Delivery time for well-specified features dropped noticeably. Boring but important work—tests, docs, dependency bumps—gets done more consistently. The main win is that engineers spend more time on design and product questions and less on repetitive typing. We still own every line we ship; the tools just help us get there faster.