Step-by-step workflow
AI-Assisted Development Workflow
A complete workflow that uses AI coding tools to boost development efficiency, from requirements analysis to code deployment.
- Difficulty
- Intermediate
- Estimated time
- Ongoing
- Steps
- 5
- Best for
- Indie developers and small engineering teams; engineers who want to speed up prototype-to-production without sacrificing code quality.
This workflow is rated "Intermediate" and takes about Ongoing, with 5 steps across 3 tools. Steps are editorial suggestions — add or drop stages to fit your project, and confirm each tool's account, quota, and platform requirements before starting to avoid interruptions.
Discover an AI product from China
MonkeyAi is an AI workspace for teams from China. Describe office, coding or design tasks in natural language, share team resources and explore private deployment.
Required tools
Steps
- 01
Requirements Analysis
Use an AI assistant to analyze requirements and plan the technical approach and architecture design.
Suggested tool: ChatGPT
- 02
Coding
Write code efficiently in an AI-assisted editor, leveraging intelligent completion and code generation.
Suggested tool: Cursor
- 03
Code Review
Use AI tools to review code, surfacing potential issues and optimization suggestions.
Suggested tool: GitHub Copilot
- 04
Test Generation
Use AI to help generate unit tests and integration test cases.
- 05
Documentation
Use AI to generate code documentation, API references, and usage guides.
Suggested tool: ChatGPT
Executable exercise and acceptance
This is an editorial procedure based on official documentation, not a completed product test. Inputs and numbers are exercise targets; expected results are acceptance criteria, not observed performance. Listed durations are planning estimates. Record actual versions and settings; cloud outputs can vary.
Prepare inputs
Input: a small todo app on an isolated branch, a requirement to reject blank tasks, its lockfile, and existing test commands. Change validation only; do not connect production services.
Environment, versions, and prerequisites
Use the project's declared runtime and package-manager versions. Record the commit, Cursor/editor version, and model. Copilot review is assistance, not a substitute for human approval.
Run in this order
- Define expected behavior for an empty string, whitespace, and a valid task.
- Ask Cursor to change only the validation boundary; inspect the diff and reject unrelated dependency or credential changes.
- Request an independent or Copilot review. The practitioner runs existing project checks in their environment, records results, and obtains human merge approval.
Copyable English exercise fixture
Requirement: trim task text; reject empty results; preserve valid text and existing task IDs. Cases: '' -> rejected; ' ' -> rejected; 'Read docs' -> accepted. Do not change dependencies.Expected delivery and pass criteria
- Deliver a minimal diff, three boundary cases, a check log, and a rollback commit. These are practitioner acceptance criteria; this site has not executed the example.
Failure conditions and fallback
- Stop for a valid-task regression, tests that merely mirror implementation, or secrets in the diff. Label unrun checks honestly; AI review does not prove tests passed.
Official references and review date
· Sources support tool capabilities and limits. Fixtures, sequence, and acceptance targets are our editorial design, not vendor performance promises.
Common mistakes to avoid
These are typical pitfalls our editors hit when assembling similar tool chains — check against them before you start and save yourself rework:
- Asking AI to generate an entire project at once instead of module by module, making regressions costly
- Merging generated code without tests, letting risk accumulate
- Accepting completions blindly without reviewing dependencies and security impact