The Real Upgrade in My AI Workflow Was Workflow Orchestration
The author realized that the real bottleneck in their AI workflow was not code generation, but workflow orchestration. They built an AI-powered workflow that can carry context, verify work, and reduce repetition.
Why it matters
Improving workflow orchestration with AI can significantly boost developer productivity and quality by automating repetitive tasks and catching issues early.
Key Points
- 1The author's AI workflow evolved from reusable commands and templates to skills, memory, auto-triggered behavior, and automatic verification
- 2One prompt kicked off a structured workflow that handled requirements, design, planning, implementation, verification, tests, and code review
- 3The workflow could challenge itself, catch structural problems early, and still allowed the author to drive product decisions
Details
The author initially found their AI workflow useful for faster code generation and reducing repeated prompting. However, they realized the real bottleneck was workflow orchestration - they were still responsible for remembering the next steps and managing context. The workflow evolved to include skills, memory, auto-triggered behavior, and automatic verification. One prompt triggered a comprehensive workflow that handled the entire development lifecycle, from requirements to code review. The workflow could pull in relevant rules from memory, loop backward for verification, and catch gaps between code and design. While the AI drove the process, the author still made the key product decisions. This workflow orchestration was the real upgrade, going beyond just code generation.
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