Scriptonia is an AI-native product management workspace that turns rough ideas into production-ready PRDs, architecture blueprints, and engineering tickets in under 30 seconds. It is often described as the 'Cursor for Product Managers.' It is designed for product managers, founders, engineering leads, and startup teams.

How fast does Scriptonia generate a PRD?

Scriptonia generates a complete PRD — including objective, problem statement, user stories, success metrics, technical requirements, and timeline — in under 30 seconds using frontier AI models.

Does Scriptonia integrate with Linear, Notion, and Jira?

Yes. Scriptonia exports directly to Linear, Notion, GitHub Issues, Jira, Confluence, and Slack. It can also export to Markdown for use with any other tool.

Is there a free plan?

Yes. Scriptonia's free plan includes 30 credits per month, unlimited PRDs, Markdown export, and a public share link. No credit card required. You can also try it without creating an account at scriptonia.dev/try.

What AI models does Scriptonia use?

Scriptonia Pro and Team plans give access to multiple frontier AI models including Claude Sonnet (Anthropic), Gemini 2.5 Flash (Google), Grok 4 (xAI), DeepSeek R1, and Kimi K2. The free plan uses Grok 4 Fast.

Who is Scriptonia for?

Scriptonia is built for product managers, founders, engineering leads, and startup teams who need to move from idea to specification quickly. It removes the hours of manual writing that traditionally goes into a PRD.

A PRD (Product Requirements Document) is a specification that describes the purpose, features, functionality, and behavior of a product to be built. Scriptonia automatically generates structured PRDs from plain-language product ideas.

How to write a PRD for an AI feature (the sections other templates miss)

AI features require PRD sections that don't exist in any standard template — and missing them is the primary cause of AI feature rework. The most commonly absent: model behavior boundaries (what the AI should and should not do), output quality success metrics (how you measure if the AI is good), and failure mode policies (what happens when the AI gets it wrong). These gaps are why most AI feature launches have a "v2 rewrite" six weeks in.

"Every AI feature we've shipped that went well had a detailed spec on model behavior boundaries and a defined quality measurement process. Every one that went poorly didn't. That correlation is not subtle."
— Arjun P., AI Product Lead at a B2B SaaS company

AI-specific sections to add to every AI feature PRD

Model behavior boundaries: What should the AI do? What should it explicitly refuse to do? What's the behavior when input is outside the expected distribution? Document this like a policy — specific and enumerable, not aspirational.

Output quality definition: How do you measure whether the AI output is good? For text generation: a rubric. For classification: precision/recall targets. For recommendation: click-through rate, acceptance rate. Define this before building — don't discover it in post-launch review.

Failure mode policy: When the AI fails (low confidence, ambiguous input, out-of-distribution request), what does the user experience? Graceful degradation? Human fallback? Error message? Specify the exact UX for each failure mode.

Human oversight: Is human review required before AI output is shown to users? For any AI feature that could cause harm, specify the moderation layer explicitly.

AI feature edge cases

Edge case	Example	Expected behavior
Low-confidence output	Model confidence <70%	Show "We're not sure — verify this" label, offer alternatives
Ambiguous input	User input could mean two different things	Clarifying question or show two output options
Out-of-distribution input	User pastes non-English text into English-only feature	Detect language, show "Currently English-only" message
Hallucination risk area	Feature asks AI to generate specific data (dates, names)	Ground with retrieval, add verification step, display source
User disagrees with AI output	User wants to override the suggestion	Override is always available, override events are logged

AI success metrics that actually measure quality

Vanity metrics for AI: requests made, outputs generated. Signal metrics: output acceptance rate (% of AI suggestions the user keeps vs. discards), edit distance (how much users modify AI output before using it), task completion rate with vs. without AI assist, and qualitative satisfaction score (CSAT on AI output quality).

>70%

Target acceptance rate for AI suggestions (good baseline)

<20%

Target edit rate for AI-generated content (measures quality)

Frequently asked questions

What's different about writing a PRD for an AI feature?

AI feature PRDs need sections that don't exist in standard templates: model behavior boundaries (what the AI should/shouldn't do), output quality metrics (how you measure if the AI is good — acceptance rate, edit distance, CSAT), failure mode policies (what happens when the model is uncertain or wrong), and human oversight requirements. Missing these is the leading cause of AI feature rework.

How do you define success metrics for an AI feature?

The most useful AI quality metrics: output acceptance rate (% of AI suggestions users keep without major changes — target >70%), edit distance (how much users modify AI output — lower is better), task completion rate comparison (with AI vs. without), and user satisfaction score on AI output quality. Vanity metrics (number of AI calls made) don't measure whether the AI is actually useful.

How do you spec AI failure modes in a PRD?

Define the failure mode categories first: low-confidence output, ambiguous input, out-of-distribution request, and model error. For each, specify the exact user experience: what message is shown, whether a human fallback is offered, and whether the failure is silent or visible. Silence is the most dangerous failure mode — users can't correct what they don't know is wrong.

What are model behavior boundaries in an AI PRD?

Model behavior boundaries define what the AI should and should not do. Examples: 'The AI should generate product requirements documents based on user input. It should not make product strategy recommendations, competitor comparisons, or financial projections. When asked to do these things, it should respond: [specific message].' These prevent scope creep in the model's behavior and set clear expectations for users.

Should I include prompt engineering details in an AI feature PRD?

No — prompt implementation is an engineering detail, like database schema. The PRD should specify behavior (what the AI should do), not implementation (how the prompt achieves that). Include the behavior requirements and quality metrics; let engineering own the prompt architecture. Review the prompts in tech spec review, not PRD review.