AI app PRD template

Overview

Building an AI app isn't just picking a model and writing prompts — you're making a stack of decisions that compound fast: which model(s), what happens when a call fails, how you meter usage, whether you stream, and what the user actually sees while waiting. Getting that down in a spec before you open your IDE saves you from rebuilding the same plumbing three times.

Draftlytic turns your one-line AI app idea into a structured, editable project spec tailored to this kind of product. It asks the right questions upfront — about your inference stack, your user-facing AI interactions, fallback behaviour, and how you're charging for it — then generates a build-ready brief you can push straight to Cursor, Windsurf, or any AI coding tool.

What makes an AI app spec different

A generic PRD template won't ask you whether your AI responses stream or batch, what you do on a timeout, or how you prevent prompt injection from user input. Those are the decisions that define your architecture.

When you generate an AI app spec in Draftlytic, it surfaces the specifics: the external AI services your app depends on, the authentication model (API keys per user vs. a shared backend key), and whether you need usage metering baked into your data model. You get a typed data model covering entities like users, sessions, and AI call logs — plus a navigation map that shows which screens hit which API endpoints, so your AI coding tool knows what to scaffold and what to skip.

The constraints section is especially useful here. AI coding tools are eager to add features; an explicit NON-GOALS list tells them not to build a fine-tuning pipeline or a model-management dashboard unless you actually asked for one.

From idea to build-ready AI app brief

Describe your app idea, answer a short set of targeted questions, and Draftlytic generates a spec that covers: prioritised features (must-have through future, each with testable acceptance criteria at Detailed depth), your tech stack and the AI services it depends on, design tokens and UX patterns, and a sequenced implementation plan you can export separately.

Pricing tiers and per-feature tier assignment are built in — critical for AI apps where usage costs mean you almost always need a free tier, a paid tier, and a clear line between them. The AI Scan catches gaps like missing rate-limit logic or unspecified fallback behaviour before you export. When you're ready, export as Markdown, PDF, or ZIP, or push the spec directly to a connected GitHub repo so your coding tool can start on it immediately.

FAQ