OpenAI Codex learning paths
Choose ordered beginner and professional OpenAI Codex tutorials for practical software engineering.
- Author
- absequ
- Published
- Updated
- Category
- AI Tools
- AI Tools
- OpenAI Codex
- tool overview
Start here to choose an ordered learning path for OpenAI Codex. Basic programming is assumed; prior knowledge of this specific tool is not. Each path has six substantive chapters and includes prerequisites, practice tasks, expected results and troubleshooting.
On this page
Choose your learning path
Codex CLI provides a terminal-first way to inspect a local repository, propose changes and run approved development tools. The IDE extension and other officially documented surfaces can fit different work, but account access, repository connections and execution environments differ. This collection begins with the CLI so the relationship between a task, a command and a Git diff stays visible.
Use Getting Started first if you have not completed a small edit–test–review task with this tool. Choose Professional Engineering after you can inspect a diff, preserve user changes and explain what a focused test does and does not prove.
- Use a disposable notes project and synthetic data for exercises. Do not use employer credentials or production infrastructure.
- Check account entitlement and data-handling policy before supplying a repository. Tool access does not authorize every possible action.
- Commands in these articles are teaching examples. Follow the target repository package manager and validation rules when transferring the workflow.
Getting Started & Core Workflows
Read these six chapters in order. No prior use of this tool is needed.
Professional Engineering Workflows
Read these six chapters in order. Prerequisite: the core path or equivalent demonstrated experience.
- Design repository instructions and a staged plan
- Build a frontend-to-API feature without crossing trust boundaries
- Debug and refactor with unit and integration evidence
- Review, commit and recover without losing user changes
- Add skills and MCP while isolating parallel tasks
- Automate bounded work and manage team cost and privacy
Access, verification and next steps
Official references were checked on 2026-11-06. Pricing, model lists and quotas are deliberately not fixed here. If a documented feature is unavailable, check platform/version, account eligibility, organization settings and the current reference before trying alternate flags or installing extensions.
A finished AI task means a reviewed result with relevant evidence. The exercises do not claim production deployment, enterprise compliance or provider correctness from a unit test. Use the existing project checks and record deferred validation. Advanced topics fit the professional path; no arbitrarily numbered seasons or empty future chapters are published.
References
Codex CLI — OpenAI
Codex authentication — OpenAI