Spec-Driven Development with ZenFlow: Why It Works

This guide covers how Spec-Driven Development (SDD) works in ZenFlow, including the core philosophy, workflow stages, and when to use it versus a “Quick Change” approach.
The Problem with Prompt-and-Pray

Most developers use AI coding agents like autocomplete on steroids: type a prompt, get code, fix what’s wrong, repeat. This works for simple tasks. For anything complex, it falls apart.

Typical AI-Assisted Development Iterations:

• Round 1: “Add a notification system.”
  • AI builds toast notifications. Not what you meant.
• Round 2: “No, backend notifications for orders.”
  • AI creates a basic email service. Missing pieces.
• Round 3: “Also SMS, and user preferences.”
  • AI bolts on features to code that wasn’t designed for them.

Each iteration loses context. The final code is a patchwork of fixes, not a coherent design. This is prompt-and-pray: throw requirements at the AI incrementally, hope it figures out what you mean, and clean up the mess afterward.

Why Specifications Work

SDD is a methodology where you define the complete requirements and technical specifications upfront—then let the agent execute against that specification.

A specification answers key questions before coding starts:

• Who needs this and why?
• What does success look like (quantifiable)?
• What’s in scope—and what’s explicitly out?
• How should this integrate with existing systems?
• What constraints must be respected?

When an AI has this information upfront, there’s nothing to interpret—just execute.

• Without a spec: The AI guesses based on training data patterns. Every missing piece of context is a potential wrong assumption.
• With a spec: The AI knows exactly what to build, which services to use, what constraints to respect, and what success means. No guessing.

The core insight is that AI agents are literal executors, not intuitive collaborators. Specifications remove ambiguity. The agent becomes an executor, not an interpreter.
How ZenFlow Implements SDD (The Full SDD Workflow)

ZenFlow formalizes SDD into a four-stage process, producing three key artifacts:

Artifact	Description
requirements.md	Product Requirements Document (PRD). Captures user stories, success criteria, and explicit out-of-scope items.
spec.md	Technical specification. Details architecture, database schemas, and API contracts.
plan.md	Implementation plan. Breaks the work into ordered, verifiable steps.

The Four Stages:

1. Stage 1: Requirements Gathering
   • The agent prompts you with questions (e.g., “What’s explicitly out of scope?”).
   • Your responses are compiled into requirements.md. You review and edit this file before approval.
2. Stage 2: Technical Specification
   • The agent analyzes your codebase and produces spec.md.
   • This is the cheapest place to catch design mistakes—review the proposed database changes and architecture before any code is written.
3. Stage 3: Implementation Planning
   • The agent breaks the spec into plan.md with ordered, dependent steps (e.g., database migrations before API endpoints).
   • You can reorder, add, or remove steps before implementation begins.
4. Stage 4: Implementation with Verification
   • The agent implements each step in order.
   • After each step, an AI verifier reviews the code diff against the spec, flagging issues before proceeding.
   • You can intervene, approve, or request changes at any point.

When to Use SDD vs. Quick Change

Use the Full SDD Workflow when:

• The feature touches 5+ files or multiple services.
• You need stakeholder review before implementation.
• There are compliance or audit requirements.
• Work spans multiple days.
• Breaking changes require migration planning.

Use Quick Change instead when:

• The task fits in one sentence with no ambiguity.
• Expected implementation time is under 10 minutes.
• No architectural decisions are needed.

Configuration

Agent presets control SDD behavior. In Settings → Agents, you can configure:

• Model selection (e.g., Claude, GPT-4, Gemini)
• Custom prompt overrides for each stage

Spec-Driven Development in ZenFlow uses structured artifacts (requirements.md, spec.md, plan.md) to give AI agents complete context before implementation. The four-stage workflow—requirements, specification, planning, implementation—provides checkpoints for review and verification.

For complex features, this approach produces more predictable results than iterative prompting, with documentation as a byproduct.