Llms.txt Generator

1. Executive Summary & Problem Statement

The Problem: Large Language Model (LLM) crawlers (e.g., GPTBot, ClaudeBot) consume vast amounts of server resources indiscriminately crawling low-value pages (faceted search URLs, tags, archives). Conversely, they often miss high-value "Ground Truth" pages (pricing, return policies, core product data) because traditional sitemap.xml files are too large and lack semantic priority. Furthermore, major platforms like Shopify do not natively allow merchants to edit root domain files to host an llms.txt file,.

The Solution: An automated engine that dynamically generates, hosts, and updates an llms.txt file (a Markdown-based "smart sitemap") at the root domain. This file acts as a curated tour for AI agents, explicitly telling them which pages contain the most authoritative, up-to-date, and machine-readable information,.

Value Proposition:

• Reduced Compute Cost: Directs bots only to high-value assets, improving the "crawl-to-referral" ratio,.

• Agentic Readiness: Ensures shopping agents find "transactable" data (price/stock) rather than hallucinations.

• Platform Bypass: Circumvents CMS limitations (e.g., Shopify) that block root file access.

2. User Personas

1. The E-Commerce Manager (Shopify/Magento): Frustrated that they cannot upload text files to their root domain; needs to ensure their top 20% of products (driving 80% of revenue) are visible to ChatGPT.

2. The Technical SEO / AI Architect: Needs a way to signal "canonical" content to LLMs distinct from the massive sitemap.xml used for Googlebot.

3. Functional Requirements

3.1 Core Generation Engine

• FR-01: Automated Asset Discovery: The system must crawl the client site via API or sitemap to identify candidate pages for the llms.txt.

• FR-02: Semantic Categorization: The generator must group URLs under Markdown headers relevant to LLM training data needs, such as:

    ◦ # Core Product Data

    ◦ # Documentation / How-to Guides

    ◦ # Company Policies (Returns, Shipping)

    ◦ # Organization Schema / Ground Truth

• FR-03: Dynamic Synchronization: The file must regenerate automatically based on inventory triggers. If a product goes "Out of Stock" (OOS), it must be removed from llms.txt immediately to prevent AI agents from recommending unavailable items,.

3.2 The Hosting & Deployment Layer

• FR-04: The Proxy Solution: Since Shopify/SaaS platforms lock the root directory, the tool must provide a proxy service or App Proxy extension that resolves domain.com/llms.txt to the generated file hosted on our infrastructure.

• FR-05: Markdown Standardization: The output must be strictly formatted in Markdown, following the emerging community standard for llms.txt (concise summaries followed by links),.

3.3 Strategic Control Interface

• FR-06: Priority Weighting: Users must be able to manually "pin" specific URLs (e.g., a Black Friday landing page) to the top of the file to prioritize ingestion during crawl spikes.

• FR-07: Exclusion Logic: The system must allow regex-based exclusion to ensure low-quality pages (e.g., /collections/all, /tags/) never appear in the llms.txt, conserving the AI crawler's token budget,.

4. Technical Specifications

4.1 File Structure Standard

The generator must produce a file following this logic:

# [Brand Name] LLM Guide
This site provides [Industry] products. The following links represent the authoritative source of truth for pricing, availability, and specifications.

## Essential Knowledge
- [Return Policy](url): Official return windows and restocking fees.
- [About Us](url): Entity definitions and corporate contact info (Organization Schema).

## Top Products (In-Stock)
- [Product A](url): [Short Description] - $Price
- [Product B](url): [Short Description] - $Price

## Technical Documentation
- [Installation Guide](url): How to install X.

• Constraint: The file size should be minimized to ensure rapid parsing by bots like OAI-SearchBot.

4.2 Integrations

• Input: Shopify Admin API (for product status), WordPress REST API, Custom Crawler.

• Output: Cloudflare Worker / AWS Lambda (for edge delivery of the text file).

5. User Flow

1. Onboarding: User connects their store/CMS.

2. Scan: System analyzes the store and suggests the "Top 100" assets based on revenue/traffic.

3. Review: User sees a draft llms.txt. They can drag-and-drop to reorder sections or uncheck low-priority pages.

4. Activate: User installs the specific app/plugin that creates the redirect or proxy to serve the file at root/llms.txt.

5. Maintenance: System runs a "Health Check" every 24 hours. If a linked page returns a 404 or OOS status, the line is auto-removed.

6. Success Metrics (The "New Scoreboard")

• Crawler Hit Rate: Frequency of access by GPTBot, ClaudeBot, and PerplexityBot to the llms.txt URL.

• Crawl Efficiency: Reduction in the number of low-value pages crawled by AI bots (saving server load).

• Inventory Accuracy: % of time an AI agent cites a product that is actually in stock (Goal: 100%).

7. Future Roadmap (Post-MVP)

• v1.5 - Smart Product Card Injection: The llms.txt will not just link to a page, but specifically link to pages where "Smart Product Cards" (embedded JSON-LD Offer Schema) have been injected, maximizing the chance of a rich snippet display.

• v2.0 - Agentic API Endpoint: Evolving llms.txt from a text file into a live JSON endpoint for autonomous buying agents to query real-time stock without parsing HTML.

AI Visibility ToolsFrancesca Tabor