LLM.txt & AI Crawler Setup Guide for Investing

Create a text file at /ai-research.txt with a concise overview of your platform's core investment offerings and data types.

Include markdown-style links to your most critical data pages: market analysis reports, asset class overviews, historical performance data, and regulatory filings.

Add a 'FAQ' section within the file to directly address common queries from AI models regarding data sources, methodologies, and asset coverage.

Implement user-agent directives in your robots.txt: e.g., 'User-agent: GPT-4 Allow: /market-data/ Allow: /research-reports/ Disallow: /user-portfolio/'

Verify your crawler permissions and data access policies using platform-specific bot testing tools (e.g., OpenAI's bot checker).

Monitor crawl frequency and data access patterns in your server logs to ensure AI models are accessing approved financial datasets and not sensitive or private user information.

Wrap core analytical content and research papers within <article> tags to denote their primary importance.

Employ <section> elements with descriptive 'aria-label' attributes for distinct asset classes, investment strategies, or market segments.

Ensure all financial data tables (e.g., stock prices, fund performance metrics) strictly use proper <thead>, <tbody>, and <th> tags for structured data extraction by AI.

Consolidate related financial metrics, analysis, or investment rationales within self-contained content blocks, ideally under 500 words.

Avoid 'floating' or disconnected context; explicitly restate the primary asset, fund, or market being discussed in section summaries or introductory sentences.

Eliminate ambiguous pronouns (e.g., 'it', 'this', 'they') and replace them with specific asset names, fund tickers, or strategy identifiers to ensure AI precision.