| File Name: | Master AI Spec-Driven Development with BMAD Method |
| Content Source: | https://www.udemy.com/course/master-ai-spec-driven-development-with-bmad-method/ |
| Genre / Category: | Ai Courses |
| File Size : | 2.4 GB |
| Publisher: | Marlon Vidal Ferreira |
| Updated and Published: | February 6, 2026 |
Are you tired of AI Agents losing context halfway through a project? You start with a great prompt, but after 20 messages, the AI forgets your initial requirements, breaks existing code, and “hallucinates” solutions. This is the Context Drift problem, and it stops most developers from building complex software with AI.
Welcome to Spec-Driven Development (SDD) with the BMAD Method.
In this course, we will transform how you code. You will stop treating AI as a chatbot and start treating it as a team of specialized engineers. You will learn the BMAD (Breakthrough Method of Agile AI-Driven Development) workflow, which splits development into two crucial phases: Upstream (Thinking) and Downstream (Building).
What you will master in this course:
- The BMAD Method: Learn why 80% of AI success happens before you write code.
- Context Engineering: How to define the Who, Why, and What to stop AI hallucinations.
- Agent Orchestration: Manage a virtual team inside Cursor AI, including the Analyst, Product Manager, Architect, Dev, and QA Agents.
- Spec-Driven Development: Create “Executable Specifications” that act as the Source of Truth for your project.
- Advanced Debugging: Fix bugs by correcting the specs, not just patching the code.
Who is this for? This course is designed for Software Engineers, Tech Leads, and Indie Hackers who want to move beyond simple “prompt engineering” and build production-grade applications using the latest Agentic workflows.
DOWNLOAD LINK: Master AI Spec-Driven Development with BMAD Method
Master_AI_Spec-Driven_Development_with_BMAD_Method.part1.rar – 1000.0 MB
Master_AI_Spec-Driven_Development_with_BMAD_Method.part2.rar – 1000.0 MB
Master_AI_Spec-Driven_Development_with_BMAD_Method.part3.rar – 496.2 MB
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