Cursor AI Development Workflows

Prompt engineering for Cursor across the full software development lifecycle: discovery, planning, implementation, debugging, testing, review, documentation, and team rules that reduce hallucinations during real repo work.

Overview

Cursor is most effective when prompts are shaped around repository evidence, tool use, and explicit stop conditions. This repository turns that into a workflow system instead of a collection of ad hoc chat prompts.

    ┌──────────────────────────────────────────────────────┐
    │                 Developer In Cursor                  │
    └───────────────────────┬──────────────────────────────┘
                            │
                            ▼
     Discover -> Plan -> Implement -> Debug -> Test -> Review
         │          │           │         │        │       │
         └──────────┴───────────┴─────────┴────────┴───────┘
                            │
                            ▼
               Prompt assets enforce behavior:
               - use file evidence
               - avoid invented details
               - verify before closing
               - separate facts from assumptions

What You Will Learn

By the end of this repository, you should be able to:

• use Cursor more like an engineering workflow partner and less like a generic chatbot
• design prompts that force repository evidence before recommendations
• handle planning, implementation, debugging, testing, and review with clearer stop conditions
• reduce hallucinations during real codebase work by improving context quality and verification discipline

Prerequisites

This repository works best if you already:

• use Cursor or a similar coding assistant in real projects
• understand the basics of debugging, testing, and code review
• want a workflow system rather than isolated prompt snippets

Recommended background:

• prompt-engineering-foundations

Recommended Companion Repository

Pair this repository with:

• llm-evals-and-anti-hallucination to measure whether your prompting changes are actually improving outcomes

Workflow Modules

File	Description
modules/01_discovery_and_planning.md	How to prompt Cursor to understand a repo and build a plan
modules/02_implementation_and_debugging.md	Prompt patterns for safe code changes and root-cause analysis
modules/03_testing_review_and_docs.md	Prompt patterns for testing, review, and documentation stages
templates/cursor-sdlc-prompts.md	Reusable prompts for each major development stage
templates/cursor-team-rules.md	Team-level rules template for consistent Cursor behavior
checklists/cursor-context-hygiene.md	What context to include, what to exclude, and when to split prompts

Cursor-Specific Reliability Principles

1. Ask Cursor to inspect current files before proposing changes.
2. Require file-backed findings for diagnosis and review.
3. Separate repo facts from assumptions.
4. Ask for minimal diffs, not broad rewrites, unless you want a redesign.
5. Require verification after edits: tests, lint, compile, or explicit reasoning if verification is unavailable.
6. Encode stop conditions when the repo evidence is insufficient.

SDLC Prompt Strategy

Stage	What To Ask Cursor For	What To Prevent
Discovery	architecture map, relevant files, dependency path	invented system design
Planning	ordered steps, dependencies, risks	implementation before understanding
Implementation	minimal edits scoped to task	unnecessary rewrites
Debugging	hypotheses ranked by evidence	shallow symptom fixing
Testing	focused regression coverage	generic tests detached from real behavior
Review	findings ordered by severity	summary-only reviews
Docs	explain changes from code and diff	generic docs not tied to implementation

Best Practices

1. Put the repo-specific evidence near the task.
2. Use prompts that name the files or search targets explicitly.
3. Ask Cursor to say "not confirmed" when a dependency cannot be verified.
4. Prefer iterative prompting over giant do-everything requests.
5. Treat team rules and instruction files as prompt infrastructure, not optional decoration.

References

• Cursor docs: code understanding, feature planning, review flows, rules and customization
• General prompt engineering guidance from OpenAI, Anthropic, Google, and Promptfoo

Author

Dhiraj Singh

Usage Notice

This repository is shared publicly for learning and reference. It is made available for everyone through VAIU Research Lab. For reuse, redistribution, adaptation, or collaboration, contact Dhiraj Singh / VAIU Research Lab.