Teach on Udemy

Turn what you know into an opportunity and reach millions around the world.

Learn More

Your cart is empty.

Keep shopping

Spec-Driven Development: designing deterministic AI systems

Name: Spec-Driven Development: designing deterministic AI systems
Rating: 4.4 (87 reviews)

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

Bestseller

Created bySkliar Serhii

Last updated 2/2026

English

English [Auto],

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

Course content

9 sections • 33 lectures • 8h 54m total length

Lesson 1: From Vibe-Coding to Spec-Driven Development17:09
Explore vibe coding versus spec-driven development and how durable intent guides AI software. See the four structural limits, the task-driven SDD pipeline, and a password reset example.
Lesson 2: The Evolution of Intent16:23
Adopt the power inversion: make the specification the master and the code the derived artifact, enabling executable intent, docs in code, and traceable, test-backed changes for deterministic AI systems.
Lesson 3: The Power Inversion: Elevating Specification Above Code16:13
Spec-Driven Development elevates the new bottleneck of intention over code, using AI as a flexible compiler to bind clear specifications, SDLC discipline, and executable tests for rapid, precise software.
Lesson 4 - The principles of spec-driven development14:27
Embrace spec-driven development by turning living specs into a universal interface that guides ai agents, using a constitution and four gates: spec, plan, tasks, and implementation, to prevent drift.

Lesson 1 - Phase 1: Specify16:08
Spec-driven development turns AI coding into a binding contract: define a functional spec with measurable success criteria and constraints before coding. Clarify ambiguity with a structured context and ears templates.
Lesson 2 - Phase 2: The architectural filter16:25
Learn to prevent architecture drift by grokking the code, enforcing a constitution, and producing a repo-aware plan with artifacts before implementation.
Lesson 3 - Phase 3: Breaking down the mountain19:41
Phase three enforces deterministic task breakdown, one task per pull request, and machine-verifiable definitions of done. Derive tasks from data, API contracts, and acceptance criteria to ensure precise, verifiable progress.
Lesson 4 - Phase 4: Implementation with validation17:58
Learn to implement with validation in phase four using guided execution, tasks.md, and specialist sub-agents, while enforcing drift control, context packaging, and micro PR reviews for deterministic AI coding.

Lesson 1 – The intern trap and bounded autonomy14:34
Design deterministic AI systems by codifying a project constitution (constitution.md) that encodes architectural DNA, enforcing rules through library-first, CLI-first principles, and governance gates (always/ask/never) for a stable AI teammate.
Lesson 2 – Agent Instructions16:35
Treat AI as an informed teammate by externalizing operational context and establishing six core areas for agent instructions. Create a master agents.md with shims and a bounded autonomy handover protocol.
Lesson 3 – Three-Tier Boundaries: Guardrails for Autonomous Agents15:13
Describe three-tier guardrails for autonomous AI agents—always do, ask first, never do—anchored by executable rules, versioned in the repo, and enforced by CI to prevent agent drift.
Lesson 4 – Standardizing the Stack17:37
Standardize the ai-driven software stack with a constitution.md that enforces a fixed stack, patterns, tests, security, and dependency rules, turning the silent tax of drift into compliance.

Lesson 1 – The EARS Syntax19:49
Discover the ears syntax, a five-template method (ubiquitous, event-driven, state-driven, optional, unwanted) that turns natural language into machine-interpretable, testable requirements using given-when-then and property-based testing.
Lesson 2 – Ambiguity Management15:24
Spec-driven development treats ambiguity as a defect, using a clarification gate, five-bucket checks, and an agent-led interview to prevent convincing wrongness.
Lesson 3 – Visual Guidance16:52
The lesson teaches visual guidance in spec-driven development, using three tiers of visual evidence, a pixel-to-requirement pipeline, and automated verification to curb ambiguity in AI-generated UIs.
Lesson 4 – Living Documents16:49
Embrace living documents in spec-driven development, turning repo markdown into repo-native anchors that guide AI and prevent drift, using spec.md plan.md tasks.md and constitution.md as the system of record.

Lesson 1 – GitHub Spec Kit16:20
Explore the GitHub spec kit for spec-driven development, enforcing a constitution, spec, plan, and tasks to anchor AI coding, maintain state, and prevent house of cards.
Lesson 2 - Agent OS: Managing the Feature Lifecycle14:47
Adopt agent os to move ai planning from chat to a file-based spec-driven lifecycle, with mission.md, roadmap.md, and techstack guides; shape specs, decompose tasks, and verify to keep living documentation.
Lesson 3 - Amazon Kiro: The High-Rigor Agentic IDE16:15
Explore spec-driven development with Quero's high-rigor agentic IDE, enforcing ear-statement contracts and proving code via property-based testing, shrinking, steering, and MCP for safe governance.
Lesson 4 - Model Context Protocol (MCP)16:31
Discover how the model context protocol turns AI into an evidence-driven debugger by connecting host, server, and tools (Playwright, DevTools, Figma) with SDD and a three-tier governance.

Lesson 1 - Test-Driven Specs: Requirements as Executable Guardrails15:27
Convert specs into executable guardrails using given-when-then scenarios that cover happy path, edge cases, and safe failure. Enforce test-first governance and property-based testing to prevent drift in AI-driven development.
Lesson 2 - Verification Gates: Mandatory Checkpoints for Quality16:45
Mastering spec-driven development, this lesson teaches verification gates—spec, plan, and atomic task checks—plus runtime governance to guard against context drift and enforce traceability.
Lesson 3 - Provable Verification via EARS & PBT18:21
Turn vague requirements into precise EARS templates, translate them into invariants, and validate them with property-based testing and shrinking to prove the system's behavior under thousands of inputs.
Lesson 4 - Multi-Agent Orchestration in Spec-Driven Development16:23
Orchestrate a lead agent and specialists using forced decomposition, context packaging, and contract-first flow to boost reliability, enforce boundaries, and keep execution deterministic via tasks.md.

Lesson 1 - Runtime Diagnostics: The Spec as a Diagnostic Anchor14:30
Use spec-driven development to diagnose runtime behavior with a diagnostic anchor. Reproduce with Playwright, inspect with DevTools, map findings to the spec, and update it to close the loop.
Lesson 2 - Rolling Out Spec-Driven Development (SDD)19:15
Roll out spec-driven development by turning architecture into an executable, self-enforcing system that detects drift through invariants, continuous validation, and consumer-driven contracts.
Lesson 3 - Legacy Modernization18:37
Apply spec-driven development to legacy modernization by extracting intent, building a behavior-driven spec, using the strangler fig pattern, and validating conformance with golden tests and drift monitoring.
Lesson 4 - Multi-Variant Implementations17:47
Explore spec-driven development and multivariant implementations that translate a single spec into Go and Rust variants while preserving functional, contract, and quality invariants, with spec.md as the IR.

Lesson 1 - The business case19:20
Explore spec-driven development as a business-focused path to deterministic AI, shifting focus from code to executable intent, reducing rework and defects with guardrails and maintenance savings.
Lesson 2 - Phased Rollout16:23
Adopt spec-driven development with a 24-week phased rollout, moving from vibe coding to spec-source maturity using constitution, gates, and automation to align intent, design, and code.
Lesson 3 - Common Pitfalls15:13
Explore how spec-driven development confronts the lethal trifecta of speed, non-determinism, and cost in AI engineering, and learn to enforce specs, EARS syntax, spec drift management, and security boundaries.
Lesson 4 - Metrics for Success15:03
Spec-driven development shifts delivery to intent in specs.md, measuring spec quality, drift, and ROI with five MVP metrics: lead time per feature, first pass rate, rework, defects by origin, drift.

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

Spec-Driven Development: designing deterministic AI systems

What you'll learn

Explore related topics

Course content

Module 1 - The paradigm shift: from vibe to intent4 lectures • 1hr 4min

Module 2 - Operationalizing the SDD pipeline4 lectures • 1hr 10min

Module 3 – Three-tier boundaries and bounded autonomy4 lectures • 1hr 4min

Module 4 – Writing Effective Specifications for AI4 lectures • 1hr 9min

Module 5 – Tooling and Ecosystem4 lectures • 1hr 4min

Module 6 - Implementation, Testing, and Verification4 lectures • 1hr 7min

Module 7 - Advanced SDD Use Cases4 lectures • 1hr 10min

Module 8 - Adoption Strategy and ROI4 lectures • 1hr 6min

Additional materials1 lecture • 1min

Requirements

Description

Who this course is for: