Spec-Driven Development: designing deterministic AI systems

Build deterministic AI systems using executable specs, EARS syntax, verification gates, and runtime diagnostics

What you’ll learn
✓ Design a complete Spec → Plan → Tasks → Implement workflow for AI-assisted software engineering
✓ Write machine-interpretable specifications using EARS syntax and eliminate ambiguity with clarification gates
✓ Create a Project Constitution and agent instruction files to govern AI behavior with Always do, Ask first, Never do boundaries
✓ Orchestrate multiple specialized AI agents safely while maintaining architectural integrity

Requirements
● Basic understanding of software development concepts such as APIs, testing, and version control
● Familiarity with AI coding tools like ChatGPT, Claude, Copilot, or Cursor is helpful but not required

Description
This course contains the use of artificial intelligence.

This course teaches you how to move from ad-hoc AI prompting to a structured, production-grade engineering workflow.

Instead of treating AI as a code generator, you will learn how to design systems where specifications are the source of truth, architecture is derived from intent, and validation is built into every phase of development.

Spec-Driven Development (SDD) replaces improvisation with a repeatable pipeline

Specify -> Plan -> Tasks -> Implement -> Verify

By the end of this course, you will know how to govern AI-assisted development in a way that is auditable, scalable, and aligned with real-world production standards.

What this course contains

Module 1 – Foundations of Spec-Driven Development

You will understand why vibe-coding works for prototypes but collapses under production constraints.

You will learn the power inversion where code serves the specification.

You will explore the evolution from SDLC, PRDs, TDD, and BDD to SDD.

You will understand the core principles of living documents, executable intent, ambiguity management, and validation gates.

Module 2 – The core SDD workflow

You will master the full lifecycle

Specify – define the north star and measurable success criteria without mixing in implementation details.

Plan – generate architecture that fits your existing codebase and respects your project constitution.

Tasks – break complex features into atomic, reviewable units.

Implement – execute tasks with human-in-the-loop validation and continuous drift detection.

Module 3 – Project context and governance

You will design a Project Constitution that defines your architectural DNA.

You will create structured Agent Instruction files to control AI behavior.

You will implement the three-tier boundary model: Always do, Ask first, Never do.

You will standardize your stack so AI-generated code feels native and compliant across teams.

Module 4 – Writing effective specifications for AI

You will learn how to write machine-interpretable requirements using EARS syntax.

You will eliminate ambiguity using clarification gates and requirement completeness checks.

You will align UI and UX generation using visuals, mockups, and Figma integrations.

You will manage specifications as living, version-controlled system-of-record artifacts.

Module 5 – Tooling and ecosystem

You will work with real-world SDD tooling, including

GitHub Spec Kit and its structured slash-command workflow.

Agent OS for feature shaping and task orchestration.

Amazon Kiro for high-rigor requirement validation and property-based testing.

Model Context Protocol (MCP) for runtime diagnostics and evidence-based debugging.

Module 6 – Implementation, testing, and verification

You will implement test-driven specs where requirements become executable guardrails.

You will apply mandatory verification gates before code is merged.

You will use property-based testing derived from EARS invariants.

You will orchestrate multiple specialized sub-agents while maintaining architectural integrity.

Module 7 – Advanced SDD use cases

You will anchor runtime diagnostics directly to the specification.

You will implement drift detection to enforce architecture at runtime.

You will modernize legacy systems without inheriting technical debt.

You will generate multi-variant implementations with language and performance parity from a single source of truth.

Module 8 – Adoption strategy and ROI

You will build a business case for SDD using real-world productivity data.

You will design a phased rollout from pilot to organization-wide adoption.

You will avoid common pitfalls, including vague prompting, spec drift, and the lethal trifecta of speed, non-determinism, and cost.

You will track the metrics that prove the SDD lift across productivity, quality, and operational reliability.

Assessment

The course concludes with a comprehensive quiz to reinforce key concepts and ensure practical understanding of the SDD workflow.

By completing this course, you will be able to

Design AI-governed engineering workflows.

Write executable, machine-interpretable specifications.

Enforce architectural discipline through validation gates.

Scale AI safely across teams and complex codebases.

Measure and prove the return on investment of structured AI adoption.

This course is designed for engineers, architects, technical leads, and engineering managers who want to move from fast but fragile AI output to controlled, deterministic, production-grade systems.

Who this course is for
■ Software engineers who want to move beyond prompt engineering into structured AI-assisted development
■ Technical leads and architects responsible for production systems
■ Engineering managers evaluating how to safely scale AI across teams

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