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AI Product Management Templates

Free AI product management templates. AI PRDs, feature specs, ethics reviews, vendor comparisons, launch checklists, and LLM evaluation plans.

Building AI products requires specialized planning artifacts that traditional PM templates do not cover. You need to define model requirements, set evaluation criteria, assess ethical risks, compare vendor APIs, and plan launches that account for AI-specific failure modes like hallucination and bias.

These templates are designed specifically for product managers working with machine learning, LLMs, and AI features. Each follows the same structure as IdeaPlan's other templates (blank template + filled example) but includes AI-specific sections like model selection criteria, evaluation metrics, safety guardrails, and responsible AI checklists.

For a complete guide to AI product management, read the AI PM Handbook or try the AI Readiness Assessment.

Related Interactive Tools

AI ROI Calculator →AI Readiness Assessment →LLM Cost Estimator →AI Build vs Buy Tool →Read the Handbook →

Templates coming soon for this category.

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Frequently Asked Questions

How is an AI PRD different from a regular PRD?+
An AI PRD includes additional sections for: model requirements (accuracy thresholds, latency targets), training data needs, evaluation methodology, failure modes and fallback behavior, ethical considerations, and ongoing monitoring plans. The user-facing requirements section is similar to a regular PRD.
What should an AI ethics review cover?+
An AI ethics review should assess: potential for bias in training data and outputs, privacy implications of data collection, transparency (can users understand why the AI made a decision?), safety risks (what happens when the model is wrong?), and regulatory compliance (GDPR, AI Act, industry-specific rules).
When should I build vs. buy AI capabilities?+
Buy (use a vendor API) when the capability is not your core differentiator, you need to ship fast, and the vendor's model quality meets your bar. Build when you need fine-grained control over the model, your data is proprietary, or the AI capability is central to your product's value proposition.
How do I evaluate LLM quality?+
Use a structured evaluation plan with: a test dataset (50-100 representative inputs), defined scoring criteria (accuracy, relevance, safety, tone), human reviewers with clear rubrics, and automated metrics where possible. Compare multiple models and prompt strategies before choosing.

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