I Tested the BMAD Method for a Week Here's What Happened
AI Development: The BMAD Method
Overview of AI's Role in Professions
- Discussion on the perception that AI will replace various professions, including developers and project managers, yet jobs remain intact in 2025.
- General-purpose models like Gemini and Claude are effective but lack expertise in specific fields, leading to new AI development methodologies.
Introduction to the BMAD Method
- Introduction of the BMAD method as a precursor to Spec Kit from GitHub; emphasizes assigning specific roles or personas to AI.
- The process involves passing work between specialized agents for focused context and optimal performance.
Project Migration Experience
- Personal experience shared about migrating a Slack bot named The Gray Cat using the BMAD method.
- Encountered issues with the deprecated “generative-ai-go” package; switched to Vercel AI SDK for improved functionality.
Features and Goals of the Bot
- The bot is designed to respond to mentions, trigger words, and generate images of British Shorthair cats based on prompts.
- Aims include feature parity with the old bot and adding capabilities like searching Notion knowledge base and reading GitHub READMEs.
Structure of the BMAD Method
- Explanation of how BMAD organizes a team of AI agents into distinct roles: planning agents (PM & architect), optional analyst, Scrum Master, developer agent, and QA agent.
- Initial setup involves running
npx bmad-method installfor configuration within an IDE.
Planning Phase Insights
- Engaged with PM agent for creating a detailed product requirement document (PRD); produced a comprehensive 442-line markdown file covering functional requirements.
- Transitioned to architect role; faced challenges due to extensive input required which slowed down responses as context filled up.
Documentation Challenges
- Resulted in a nearly 1600-line architecture document; while it appeared professional, concerns arose over managing large documents effectively.
Development Cycle Issues
- After switching to developer agent, initial code structure was promising but encountered issues with linter failures leading to constant back-and-forth corrections.
Quality Assurance Experience
- QA agent initially praised implementation but failed when attempting to run the app; highlighted need for manual verification despite automated processes.
Lessons Learned from Using BMAD
- Concluded that structured documentation through BMAD is beneficial compared to one-shot prompting methods.
- Planning Phase Importance: Emphasized thoroughness during planning stages as crucial for preventing future problems.
BMAD Method: Enhancing Developer Productivity
Key Insights on Code Review and Development Process
- The importance of verifying a developer's work is emphasized, suggesting frequent commits to track changes effectively before they are integrated with other work.
- Developers should avoid shortcuts like using “any” types in code; failing to address these issues can lead to an unmanageable codebase.
- The role of the developer shifts from writing every line of code to guiding and reviewing AI agents, highlighting that while productivity can increase, it does not replace the need for skilled developers.
- Mastery of the BMAD method can significantly enhance productivity, but it requires skill and diligence to use AI tools effectively.