Overview
Most developers use AI as a code completion tool. I use it as a cognitive partner across the entire software development lifecycle.
Stage 1: Research
Before writing a single line of code, I deploy AI agents to:
- Survey existing solutions and libraries
- Summarize documentation and API references
- Identify potential pitfalls and edge cases
- Generate competitive analysis
This stage saves hours of manual research and gives me a comprehensive understanding of the problem space before I start building.
Stage 2: Architecture
This is where I spend the most mental energy. I use AI as a sounding board — not to make decisions for me, but to challenge my assumptions:
- "What are the trade-offs of using X vs Y?"
- "How would this scale to 100x users?"
- "What failure modes am I not considering?"
Stage 3: Implementation
With architecture locked, AI agents generate the bulk of the code. I guide them with:
- Clear interface definitions
- Type contracts
- Test expectations
- Coding standards
Stage 4: Review & Polish
Every line of AI-generated code gets reviewed. I look for:
- Logic errors and edge cases
- Security vulnerabilities
- Performance bottlenecks
- Accessibility issues
The Bottom Line
AI doesn't replace engineers. It amplifies them. The bottleneck shifts from "can I type fast enough" to "can I think clearly enough."