Release Notes Template for AI Products

Why AI products release notes require a specific approach

Your audience is AI product teams and ML engineers. The stakes, terminology, and expectations of AI products products are different from generic SaaS. This page gives you a copy-paste template and proven practices built for your context.

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Core release notes template

## [Version or Date] — [One-line summary]

### ✨ New
- **[Feature name]:** [What it does and why it matters to your audience]

### ⚡ Improved  
- **[Area]:** [Specific improvement with a measurable or concrete outcome]

### 🐛 Fixed
- [Bug description, affected users, and resolution]

### ⚠️ Important
- [Breaking change, required action, compliance notice, or critical update]

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3 real AI products release note examples

Example 1 — Model update

New model: Claude Sonnet 4 — We've upgraded the underlying model powering AI suggestions. In our evaluations, output quality improved by 31% on code generation tasks and 24% on writing tasks. If you've customized system prompts, review them — responses may differ in style and length from the previous model.

Example 2 — Quality improvement

Reduced hallucinations on numeric data — The AI now cites sources and flags uncertainty when generating statistics or financial figures. Previously, unverified numbers were stated as facts. This change may make some responses slightly more cautious — this is intentional.

Example 3 — Safety update

Content policy update — new refusal categories — The AI will now decline to generate certain categories of content it previously produced. See the updated content policy for the full list. If your use case is affected, contact support to discuss your options.

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Ai Products release note best practices

1. Be explicit when model behavior changes — users have built workflows around your AI's output style 2. Quantify quality improvements with eval benchmarks when possible — 'better' is not enough 3. Any change to what the AI will or won't do requires prominent communication — this affects user trust 4. For developer-facing AI products, include before/after output examples for major changes 5. Acknowledge when a change trades off quality in one area for improvement in another

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What good looks like

The best AI products products publish release notes that match their audience's expectations: specific, actionable, and framed around what users care about most. Study how OpenAI model releases, Anthropic release notes, and Cursor changelog structure their changelogs as reference points.

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