How to Structure Prompts for Codex AI

Mar 25, 2026

I spent an entire afternoon watching Codex produce broken, incomplete code. The culprit? My prompts were too vague and tried to do too much at once.

“Build me a social media app with user profiles, posts, comments, likes, notifications, and a feed algorithm. Make it fast and secure.”

That single prompt cost me hours of debugging and eventual restart-from-scratch.

The Real Problem

When I first started using Codex, I treated it like a magic genie - one wish, one prompt, everything done. But that approach led to:

Incomplete implementations (half-baked features)
Loss of context as the conversation grew
Contradictory code decisions across files
Wasted time on back-and-forth clarifications

The code would compile, sort of work, then fall apart when I tried to extend it. I’d ask Codex to fix one thing, and it would break another.

What Actually Works: Two-Phase Prompting

After many frustrating sessions, I discovered a pattern from experienced users on the r/codex community. The winning approach has two distinct phases:

Phase 1: Set Context Once

Start with a high-level description that gives Codex the full picture:

I'm building a [app type] with the following goals:
- [Goal 1]
- [Goal 2]

Tech stack: [list technologies]
Key constraints: [security, performance, etc.]

Please create the initial project structure.

This is your ONE big prompt. It’s not about getting everything built - it’s about establishing what we’re building and why.

Phase 2: Many Small Prompts

After context is set, each subsequent prompt should:

Address ONE specific problem
Be precise and simple
Reference previous context when needed
Include acceptance criteria

Here’s the template I now use:

## Current State
[Where we are in the project]

## What I Need
[One specific thing you want done]

## Constraints
- [Constraint 1]
- [Constraint 2]

## Expected Outcome
[How to verify it works]

Why This Matters

Small, focused prompts reduce hallucination. When you ask for too much at once, Codex has to make assumptions. Those assumptions compound. By the time you reach line 500, the code contradicts what happened at line 50.

With small prompts, you catch issues early before they cascade.

Precision Makes the Difference

Let me show you the evolution of my prompts:

Add error handling to the API

That prompt got me… random error handling in some places, none in others, inconsistent error messages. Codex had to guess where, what, and how.

Now I write:

Add error handling to the /api/posts endpoint:
- Return 400 for validation errors
- Return 500 for database errors
- Log errors to console with stack trace
- Don't expose internal error details to client

Same task, but now Codex knows exactly what to do.

The Iteration Trap I Fell Into

Here’s a mistake I made repeatedly:

The post creation isn't working. Fix it and also add
image uploads, user mentions, and scheduled posting.

I kept bundling “fix this” with “add that.” Every time something broke, I’d throw in new feature requests. The codebase became a mess of half-implemented features sitting on top of unpatched bugs.

Now I follow a strict rule: One prompt = one thing. If I’m fixing, I’m only fixing. If I’m adding, I’m only adding.

A Real Workflow Example

Building a simple blog engine, my prompt sequence looked like this:

I'm building a markdown blog with these requirements:
- Source: MDX files in /content directory
- Build: Static generation via Astro
- Theme: Minimal, readable typography
- Deploy: Vercel

Create the initial Astro project structure with MDX support.

Current state: Astro project initialized with MDX support.

What I need: Implement the blog post listing page.

Constraints:
- Show 10 posts per page
- Sort by date descending
- Display title, date, excerpt
- Link each post to /[slug] route

Expected outcome: Visiting / shows paginated blog posts.

Current state: Blog listing working.

What I need: Implement individual post page at /[slug].

Constraints:
- Render MDX content
- Show title, date, author
- Add previous/next post navigation

Expected outcome: Each post URL renders the full content.

Each prompt built on the previous. Each was specific enough to verify. Each was small enough that if something went wrong, I knew exactly where to look.

Common Mistakes to Avoid

Skipping context: If you dive straight into implementation prompts without setting context, Codex makes assumptions about architecture, naming, and patterns. Those assumptions may not match what you want.

Vague requirements: “Make it secure” means nothing. “Add rate limiting, validate all inputs with Zod, and use HTTPS-only cookies” gives Codex something to work with.

No acceptance criteria: Without defining what “done” looks like, you can’t verify the output. And if you can’t verify, you can’t catch hallucinations.

Too many requirements: A prompt with 15 bullet points is not “one prompt solving one problem.” It’s 15 prompts masquerading as one. Split them up.

When to Use Planning Mode

For complex features, I’ve found planning mode (Shift+Tab in Codex) invaluable. Instead of implementing immediately, Codex produces a step-by-step plan. I can review, adjust, then have Codex execute each step.

This catches architectural issues before they become code issues.

The Bottom Line

Structure your Codex prompts like this:

One context-setting prompt - describe the whole application upfront
Many implementation prompts - each solves one specific problem
Precision in every prompt - exact requirements, clear constraints, verifiable outcomes

The developers who get the best results from Codex aren’t writing better code themselves - they’re writing better instructions for the code they want.

Final Words + More Resources

My intention with this article was to help others share my knowledge and experience. If you want to contact me, you can contact by email: Email me

Here are also the most important links from this article along with some further resources that will help you in this scope:

👨‍💻 Reddit Codex Community
👨‍💻 OpenAI Prompt Engineering Guide

Oh, and if you found these resources useful, don’t forget to support me by starring the repo on GitHub!