How Do I Use ChatGPT and Codex CLI Together for Large Coding Projects? A 15-Hour Workflow Revealed

Apr 20, 2026

Developer workflow automation

The Problem

I tried building a 13-phase project in ChatGPT alone. After phase 3, it lost track of earlier decisions. By phase 7, the code had inconsistent patterns. At phase 10, I gave up - the context window was drowning in implementation details, and architectural coherence was gone.

Then I tried Codex CLI for everything. It excelled at generating code fast, but had no idea what the overall system should look like. It happily created files that contradicted each other, used different naming conventions across modules, and skipped integration testing because “that wasn’t the current task.”

The Reddit community confirmed this pain point:

“Project got too big for it to keep enough context.”

Single-tool approaches hit a wall. Context limitations kill architectural coherence. Planning and execution require different cognitive processes, but mixing them in one tool leads to suboptimal results everywhere.

What I Needed

I wanted a workflow that:

Preserves architectural vision across thousands of lines of code
Generates implementation fast without sacrificing quality
Provides quality gates to catch integration issues early
Automates the loop so I’m not manually switching between tools

The Solution: ChatGPT as Planner, Codex CLI as Builder

A Reddit developer shared their workflow for a 13-phase, 28,000-line project. The key innovation: strict role separation.

ChatGPT Pro  → Strategic layer: planning, review, quality gate
Codex CLI    → Execution layer: implementation, boilerplate, patterns

This isn’t just using two tools. It’s giving each tool a single job and orchestrating them with an automation loop.

The Three-Phase Workflow

Phase 1: Architecture & Planning (ChatGPT)
  → Decompose project into logical phases
  → Create detailed specifications for each phase
  → Define interfaces and coding standards

Phase 2: Implementation (Codex CLI)
  → Execute phase-specific tasks
  → Generate boilerplate rapidly
  → Handle repetitive patterns

Phase 3: Quality Gate (ChatGPT)
  → Review generated code against specs
  → Identify integration issues
  → Approve progression to next phase

How I Implemented the Automation Loop

The breakthrough was the 2-minute check cycle. Instead of manually switching between apps, I scripted the loop:

class HybridAIWorkflow:
    def __init__(self):
        self.planner = ChatGPTPro()  # Strategic layer
        self.builder = CodexCLI()     # Execution layer

    def run_project(self, requirements):
        # Phase 1: Planning in ChatGPT
        phases = self.planner.decompose_project(
            requirements,
            target_phases=13
        )

        for phase in phases:
            # Phase 2: Implementation in Codex
            specifications = self.planner.create_specs(phase)

            implementation = self.builder.implement(
                specs=specifications,
                context_limit=2000  # Lines per context
            )

            # Phase 3: Quality gate in ChatGPT
            review = self.planner.review_code(
                implementation,
                against=specifications
            )

            if review.passed:
                self.commit_phase(phase, implementation)
            else:
                self.builder.refine(review.feedback)

    def automation_loop(self):
        """2-minute check cycle"""
        while not project_complete:
            time.sleep(120)  # 2 minutes
            self.run_quality_check()
            self.adjust_next_phase()

The automation loop runs continuously. Codex implements for 1 minute, ChatGPT reviews for 1 minute. If review fails, Codex refines. If review passes, commit and move to next phase.

The Wrong Way: Merged Roles

I first tried using ChatGPT for everything:

def build_large_project_wrong():
    # ChatGPT tries to plan AND implement
    architecture = chatgpt.plan_entire_project()

    # Context already degrading...
    code = chatgpt.implement_all_phases(architecture)

    # No independent quality check
    return code  # Result: inconsistent patterns, lost requirements

This fails because:

Context overflow - ChatGPT’s window fills with implementation details, losing architectural vision
No feedback loop - No independent validation of generated code
Cognitive mixing - Planning and execution compete for the same context space

Phase Granularity: The Critical Factor

The Reddit example used 13 phases for 28,000 lines. That’s roughly 2,000 lines per phase. I tried fewer phases and hit context issues:

WRONG (3 phases for 28k lines):
  Phase 1: 9,000 lines → Context overflow
  Phase 2: 9,000 lines → Integration nightmares
  Phase 3: 10,000 lines → Architectural drift

CORRECT (13 phases for 28k lines):
  Each phase: ~2,000 lines → Clean context window
  Each phase: Review before next → Quality preserved
  Each phase: Small enough to verify → No surprises

The rule: aim for 10-15 phases for large projects, each producing 2,000-3,000 lines.

Multi-Instance Orchestration

The Reddit thread mentioned another optimization: running multiple Codex CLI instances with different models:

#!/bin/bash

# Terminal 1: Backend implementation
codex-cli --model gpt-4 --focus backend --port 8001 &

# Terminal 2: Frontend implementation
codex-cli --model gpt-4 --focus frontend --port 8002 &

# Terminal 3: Testing & validation
codex-cli --model gpt-3.5-turbo --focus tests --port 8003 &

# ChatGPT orchestrator reviews output from all three
chatgpt-orchestrator --sync-interval 120 --ports 8001,8002,8003

This parallelizes implementation while ChatGPT maintains the single architectural vision. Backend and frontend can progress independently, but ChatGPT ensures they stay coordinated.

Context Management Strategy

The key to this workflow is context window discipline:

ChatGPT context:
  - High-level architecture ONLY
  - Interface definitions
  - Specifications and constraints
  - NOT implementation details

Codex CLI context:
  - Localized implementation context
  - Current phase files
  - Immediate dependencies
  - NOT whole-project architecture

I never feed the entire codebase into ChatGPT for context. That overloads it and reduces effectiveness. Instead, I provide interface definitions, specifications, and critical constraints.

What I Ran Into: Integration Issues

The first few phases worked smoothly. Then I hit integration problems:

- Codex generated code that contradicted earlier phases
- Interface changes broke existing implementations
- Tests passed locally but failed when integrated

The fix: mandatory ChatGPT review before phase transitions. I added a checklist:

[ ] Code matches phase specifications
[ ] Interfaces unchanged from earlier phases
[ ] No new dependencies that break existing code
[ ] Tests pass (unit + integration)
[ ] No architectural drift from CLAUDE.md constraints

Skipping this checklist caused technical debt accumulation. Enforcing it prevented issues from compounding.

Why This Matters: The 15-Hour Savings

The Reddit developer reported this workflow “freed up about 15 hours of my day.” That’s not just speed - it’s cognitive load distribution.

SINGLE-TOOL APPROACH:
  Planning burden: High (mixing with implementation)
  Execution burden: High (mixing with planning)
  Quality burden: High (no independent review)
  Switching overhead: High (manual tool changes)
  Total cognitive load: MAXED OUT

HYBRID APPROACH:
  Planning burden: Focused (ChatGPT only)
  Execution burden: Focused (Codex only)
  Quality burden: Distributed (ChatGPT reviews)
  Switching overhead: Automated (2-minute loop)
  Total cognitive load: OPTIMIZED

Your brain operates at peak efficiency when focusing on one type of task at a time. Separating planning from execution lets each cognitive mode run at full capacity.

Quality Preservation Through Scale

This workflow scales better than single-tool approaches:

Single-tool: 10x project → 100x complexity (interdependencies compound)
Hybrid: 10x project → 10x complexity (each phase stays linear)

Why: Each tool operates within its strengths. Adding phases
increases work linearly because context windows stay clean.

The quality gate prevents drift. By phase 13 of a 28,000-line project, the code still matched the architectural vision from phase 1. That’s impossible with single-tool approaches.

Common Mistakes to Avoid

I made every mistake on this list before the workflow worked:

Mistake 1: Merged roles

WRONG:
  ChatGPT session: "Plan phase 3, then implement it"
  Result: Context overflow, lost coherence

CORRECT:
  ChatGPT session: "Create specifications for phase 3"
  Codex CLI session: "Implement phase 3 using these specs"
  Result: Clean separation, maintained vision

Mistake 2: Skipping quality gates

WRONG:
  Codex completes phase 4 → immediately start phase 5
  Result: Technical debt accumulation, integration failures

CORRECT:
  Codex completes phase 4 → ChatGPT review → fix issues → phase 5
  Result: Clean progression, no debt

Mistake 3: Overloading ChatGPT context

WRONG:
  Feed entire codebase into ChatGPT for "context"
  Result: Reduced effectiveness, higher costs, slower responses

CORRECT:
  Feed only interface definitions and phase specifications
  Result: Focused context, fast responses, maintained quality

Mistake 4: Manual tool switching

WRONG:
  Implement in Codex → copy to ChatGPT → review → copy feedback → repeat
  Result: Workflow friction, lost momentum, inconsistent timing

CORRECT:
  Script the 2-minute automation loop
  Result: Consistent rhythm, maintained momentum

Mistake 5: Insufficient phase breakdown

WRONG:
  Phase 1: "Implement entire authentication system"
  Result: Too large, context issues within the phase

CORRECT:
  Phase 1: "Implement registration endpoint"
  Phase 2: "Implement login endpoint"
  Phase 3: "Implement password reset"
  Result: Clean, verifiable phases

How I Use It Now

My workflow for a new large project:

Planning session (ChatGPT): Decompose into 10-15 phases, create specifications
Foundation phase (Codex): Project structure, core utilities, base configuration
Automation loop starts: 2-minute cycle between implementation and review
Each phase: Codex implements → ChatGPT reviews → fixes → commit → next phase
Integration phase: ChatGPT reviews all phases together for coherence

The loop runs automatically. I monitor for review failures and adjust specifications when needed. But most of the time, it just works.

What to Set Up Before Starting

Before running this workflow:

[ ] ChatGPT Pro access (for planning and review)
[ ] Codex CLI installed and configured
[ ] CLAUDE.md or similar spec file with architecture decisions
[ ] Automation script for the 2-minute loop
[ ] Phase breakdown document (10-15 phases for large projects)
[ ] Each phase specification ready before implementation

The setup takes 30-60 minutes. But it saves 15 hours per day of coding overhead.

Model selection strategy: Use Sonnet 4.5 for main development (best coding model). Use Opus 4.5 for architectural decisions (deepest reasoning). Use Haiku 4.5 for lightweight tasks (90% of Sonnet capability, 3x cost savings).

Context window management: Avoid the last 20% of context window for large-scale work. Single-file edits and simple fixes can use any context level.

CLAUDE.md pattern: Create a specification file with architecture decisions, constraints, and coding standards. Both tools reference this to maintain consistency when context fades.

Summary

Large coding projects break single AI tools because context limitations and cognitive mixing kill architectural coherence. The solution: strict role separation with ChatGPT Pro as strategic planner/reviewer and Codex CLI as implementation engine, orchestrated by a 2-minute automation loop.

The workflow preserved architectural integrity across 28,000 lines in 13 phases, with a reported 15-hour daily time savings. The key is small, verifiable phases, mandatory quality gates before transitions, and automated tool switching.

Start with phase decomposition in ChatGPT, implement each phase with Codex using clean context windows, review with ChatGPT before progressing, and script the loop so it runs continuously.

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:

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