How Liatrio's four prompts turn effective AI collaboration patterns into a readable workflow teams can study, discuss, and adapt
Spec-Driven Development is not just a four-step checklist. It is a practical implementation of reusable AI techniques that make work more reviewable, more teachable, and easier to resume without losing the thread.
This matters because SDD exposes those techniques directly in markdown prompts, files, and review points. Instead of asking teams to trust a product's hidden behavior, it gives them a concrete operating model they can read, borrow, and refine.
The sections below show the core techniques, where they appear across SDD-1 through
SDD-4, and how they contribute to a more reliable AI-assisted implementation loop.
Want the longer teaching version? Read AI-Native Development Primitives for a deeper explanation of each pattern, including SDD-specific examples and broader software-development examples.
These are the core AI collaboration techniques the workflow makes explicit. Each one can be studied on its own, but they become most useful when combined into a single, reviewable delivery loop.
Break work into a small set of demoable units before expanding into ordered tasks that can produce explicit proof.
Reveal complexity in stages so the workflow only expands from questions to specs to tasks when the human or the AI is ready to process new information.
Standardize specs, task lists, proof artifacts, and validation reports so they stay easy to review, diff, and reason about.
Persist workflow state in deterministic artifacts during the implementation loop so work can pause, resume, be reviewed, and be validated across sessions.
Load only the codebase context, files, and constraints needed for the current step instead of front-loading everything at once.
Insert explicit stop-and-wait gates where a human can review, approve, redirect, or resume the workflow without guessing what happened.
Treat verification as a first-class deliverable by requiring proof artifacts, command output, and explicit quality gates before calling work done.
Improve reliability by guiding the assistant through defined analysis sequences and keeping final outputs focused on evidence-backed conclusions.