LLM.txt & AI Crawler Setup Guide for UX designers
An authoritative technical manual for configuring your UX design portfolio and knowledge base to selectively allow, route, and optimize content ingestion by specialized AI research agents and design tool crawlers.
High Priority
Deploy /ux-ai-crawl.txt Protocol
Establish a machine-readable summary of your entire portfolio hierarchy and design process documentation specifically for AI agents and research tools.
Create a text file at /ux-ai-crawl.txt with a brief introduction to your design philosophy and core competencies.
Include markdown-style links to your most critical case studies, process breakdowns, and UX methodology articles.
Add a 'Design Process FAQ' section in the file to answer common queries about your workflow, tool preferences, and problem-solving approaches directly.


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High Priority
Design Tool & AI Agent Selective Indexing
Fine-tune which sections of your portfolio and design resources should be ingested by AI agents (e.g., for design inspiration, trend analysis) and AI-powered design tools.
User-agent: * Allow: /case-studies/ Allow: /methodology/ Allow: /design-principles/ Disallow: /playground/
Use specific directives for known AI agents if available (e.g., User-agent: MidjourneyBot for visual assets).
Monitor access logs to ensure AI agents are correctly accessing and indexing your design deliverables and process documentation.
Medium Priority
Semantic HTML for Design Artifacts
Utilize HTML5 landmarks and ARIA attributes to help AI crawlers and design tools understand the structure and context of your design artifacts and case studies.
Wrap each distinct case study within <article> tags, using a clear 'data-case-study-id' attribute.
Employ <section> with descriptive 'aria-label' attributes for different phases of a project (e.g., 'User Research', 'Wireframing', 'Prototyping', 'Usability Testing').
Ensure all visual elements (images, diagrams, mockups) have descriptive 'alt' text and use appropriate semantic tags like <figure> and <figcaption>.
High Priority
RAG-Friendly Design Documentation Optimization
Structure your design process explanations and case study narratives so they can be easily 'chunked' and retrieved by Retrieval-Augmented Generation (RAG) pipelines for AI-driven design assistance or analysis.
Isolate core design problems and their solutions within distinct content blocks, ideally under 700 words per problem/solution pair.
Clearly state the primary UX challenge and the proposed design intervention at the beginning of each relevant section.
Replace ambiguous references (e.g., 'this feature', 'the solution') with specific UI element names, feature titles, or user task descriptions.
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