Building an AI-First Development Workflow with Codex CLI

Category: ai

Reading Time: 8 min read

Tags: AI, Development Workflow, Codex CLI, Automation, Developer Experience

Building an AI-First Development Workflow with Codex CLI

Figures are based on my internal projects; your results will vary.

As developers, I'm living through a renaissance of productivity tools. The emergence of AI-powered development assistants has fundamentally changed how we approach coding, debugging, and project management. After six months of experimentation, I've developed an AI-first workflow that has roughly tripled my development velocity in two personal projects (Jan–Jun 2025) while actually improving code quality.

The Traditional Development Pain Points

Before diving into the solution, let's acknowledge the problems I'm solving:

- Context Switching: Constantly jumping between documentation, Stack Overflow, and code
- Repetitive Tasks: Writing boilerplate, updating configs, creating similar components
- Knowledge Gaps: Working with unfamiliar libraries or frameworks
- Code Review Overhead: Catching simple issues that could be automated

Enter the AI-First Workflow

My current setup centers around the Codex CLI, integrated with modern development tools:

``steps
Install Codex CLI and authenticate
Create a project-specific prompt template folder
Configure auto-edit permissions in your IDE
Define 3 recurring tasks to delegate to AI
Commit baselines before auto-edits
`

Core Tools

- Codex CLI: Primary AI assistant for code generation and problem-solving
- Cursor IDE: AI-integrated development environment
- GitHub Copilot: Real-time code completion
- Custom Prompts: Domain-specific templates for common tasks

Daily Workflow

`bash

Morning routine: Start with project context

codex "review yesterday's progress and suggest today's priorities"

Feature development

codex --auto-edit "implement user authentication with JWT and refresh tokens"

Testing and debugging

codex --full-auto "add comprehensive test coverage for the auth system"

Documentation

codex "generate API documentation for the new endpoints"
`

`js-run
console.log('Prompt strength test:');
function scorePrompt(prompt) {
const hasDetail = /\b(responsive|with|using|support|accessibility|error|tests?)\b/i.test(prompt);
const hasTech = /chart\.js|react|hooks|jwt|rst|wacc|typescript/i.test(prompt);
let score = 0;
if (hasDetail) score += 5;
if (hasTech) score += 5;
return score;
}

console.log('Weak:', scorePrompt('make a chart'));
console.log('Strong:', scorePrompt('create a responsive candlestick chart using Chart.js with zoom and pan, with tests'));
`

Real-World Example: Building a Financial Dashboard

Let me walk through how AI-first development worked for a recent project - a real-time trading dashboard.

Traditional Approach (Before AI)

1. Research charting libraries (2 hours)
2. Set up basic project structure (1 hour)
3. Implement data fetching (3 hours)
4. Create chart components (4 hours)
5. Style and responsive design (3 hours)
6. Testing and debugging (2 hours)
Total: 15 hours

AI-First Approach (In my internal demo project)

1. codex "create a trading dashboard with Chart.js and real-time data"
2. codex --auto-edit "add technical indicators: RSI, MACD, moving averages"
3. codex "make it responsive and add dark/light theme toggle"
4. Manual review and customization (1 hour)
Total: 5 hours (≈3× faster)

Key Principles I've Learned

1. Prompt Engineering is a Skill

`bash

Weak prompt

codex "make a chart"

Strong prompt

codex "create a responsive candlestick chart using Chart.js with volume indicators,
supporting both dark and light themes, with zoom and pan functionality"
`

2. Iterative Refinement

Don't expect perfect code on the first try. Use AI as a starting point, then iterate:

`bash
codex --auto-edit "refactor the chart component to use React hooks instead of class components"
codex "add error handling and loading states to the data fetching"
codex "optimize performance for real-time updates with 60fps"
`

3. Domain-Specific Context

I maintain prompt templates for common patterns in my projects:

`bash

Financial calculations template

codex "create a [CALCULATION_NAME] function following Benjamin Graham's methodology,
with input validation, error handling, and TypeScript types"

React component template

codex "create a [COMPONENT_NAME] component with props interface, responsive design,
accessibility features, and unit tests"
``

Measuring the Impact

After six months of AI-first development across three small-to-medium React projects (≈10k LOC each):

Methodology & Caveats

Measurement Context:
- Sample Size: 3 personal React projects (financial tools, portfolio site, demo applications)
- Project Scale: ≈10,000 lines of code each
- Time Frame: January–June 2025
- Tooling: Codex CLI v0.9, Jest for test coverage, Istanbul for line coverage metrics
- Baseline: Compared against my previous development approach on similar projects

Limitations:
- Results based on solo development; team dynamics may differ
- Projects were greenfield builds; legacy codebase integration not tested
- Metrics reflect personal coding patterns and may not generalize

Productivity Metrics (Internal KPI Tracking)

- Feature delivery: ≈3× faster from concept to production
- Bug density: ≈40% reduction (AI catches common errors during generation)
- Documentation coverage: 90% (auto-generated API docs and README sections)
- Test coverage: 85% line coverage via Jest/Istanbul (comprehensive test generation)

Quality Improvements

- Code consistency: AI follows established patterns
- Best practices: Built-in security and performance considerations
- Accessibility: AI includes ARIA labels and semantic HTML by default

Common Pitfalls and Solutions

Over-Reliance on AI

Problem: Accepting AI suggestions without understanding them
Solution: Always review generated code, ask AI to explain complex logic

Context Loss

Problem: AI forgets project-specific conventions
Solution: Maintain project-specific prompt templates and context files

Integration Complexity

Problem: AI-generated code doesn't integrate well with existing systems
Solution: Provide clear context about existing architecture and constraints

Building Your AI-First Workflow

Week 1: Foundation

1. Install Codex CLI and authenticate
2. Practice basic prompts for common tasks
3. Identify your most repetitive development tasks

Week 2: Integration

1. Create project-specific prompt templates
2. Integrate with your existing IDE and tools
3. Establish code review processes for AI-generated code

Week 3: Optimization

1. Refine prompts based on results
2. Build custom scripts for complex workflows
3. Share templates with your team

Week 4: Measurement

1. Track productivity metrics
2. Gather team feedback
3. Iterate on the workflow

The Future of AI-Driven Development

I'm still in the early days. I'm excited about:

- Semantic code search: Finding code by intent, not just text
- Automated refactoring: Large-scale codebase improvements
- Intelligent testing: AI that understands user flows and edge cases
- Documentation synthesis: Auto-updating docs that stay in sync with code

Conclusion

The AI-first development workflow isn't about replacing developers - it's about amplifying my capabilities. By handling routine tasks, AI frees me to focus on architecture, user experience, and creative problem-solving.

The key is treating AI as a powerful assistant, not a magic solution. With the right approach, you can dramatically increase your productivity while building better software.

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Want to see this workflow in action? Check out my project showcase where every tool was built using AI-first development principles.