# Product Guidelines ## Brand & Voice - **Tone:** Enthusiastic and Accessible yet Concise. The voice should be encouraging to learners while remaining direct and functional. Avoid excessive jargon or overly long analogies; prioritize clarity and "get-to-the-point" descriptions. - **Audience Engagement:** Speak directly to the user's curiosity. Frame technical explanations as answers to "How does this work?" or "What happens if...?" ## Visual Identity - **Aesthetic:** Clean & Modern. Prioritize high whitespace, legible typography, and a clear visual hierarchy (inspired by Material Design or Notion). - **Mode:** Support both Light and Dark modes, ensuring high contrast for data visualizations. - **Color Palette:** Use a consistent color language for different model components (e.g., specific colors for Attention vs. MLP layers) to aid mental mapping. ## User Interface & Experience - **Terminology & Disclosure:** Use a combination of Progressive Disclosure and In-Situ Definitions. - **Tooltips:** Use tooltips for most technical terms to provide immediate, brief context without cluttering the UI. - **In-Situ Descriptions:** Provide short, clear descriptions immediately followed by the relevant interactive example to solidify the concept through action. - **Experimentation Layout:** Sandbox Explorer. - Provide a comprehensive control panel for free-form exploration (toggles, sliders, ablation switches). - **Comparison View:** Integrate comparison elements into the sandbox so users can see the impact of their modifications relative to the original state. ## Design Principles - **Action-Oriented Learning:** Every architectural explanation should be paired with an interactive element. - **Visual Consistency:** Ensure that attention head indices, layer numbers, and token highlights are consistent across all panels and visualization types. - **On-Demand Depth:** Keep the primary interface simple, but provide clear paths (like the Glossary or tooltips) for users who want to dive deeper into the technicalities.