Documentation Index
Fetch the complete documentation index at: https://developer.prove.com/llms.txt
Use this file to discover all available pages before exploring further.
Concepts and definitions
To better understand the technical infrastructure of Prove’s documentation, here are the key concepts and terms used:- Machine Readability: The design of content in a format that can be processed and “understood” by computer programs or AI, rather than just being optimized for human visual consumption.
- LLM (Large Language Model): AI systems that process and generate human-like text, commonly reached through chat interfaces or APIs.
Plain text docs
Prove designs its documentation for AI consumption. Every page maintains a parallel Markdown representation accessible via the.md extension—for example, this page as Markdown. That helps AI tools and agents consume Prove content.
| Feature | Markdown (.md) | HTML/JS Rendered Pages |
|---|---|---|
| Token Efficiency | High. Minimal syntax means more actual content fits in the context window. | Low. Dense with tags (<div>, <span>) and scripts that waste tokens. |
| Data Extraction | Direct. Content is ready to be parsed as-is without extra processing. | Complex. Requires a browser engine to execute JS before content is visible. |
| Content Visibility | Complete. Includes all text, including content hidden in UI tabs or toggles. | Partial. Hidden or “lazy-loaded” content is often missing from the initial scrape. |
| Contextual Hierarchy | Explicit. Headers (#, ##) signal the importance and relationship of data. | Inferred. AI must guess hierarchy based on nested tags or CSS classes. |
| Formatting Noise | Minimal. Focuses on the data, reducing the risk of the AI getting distracted. | Significant. Inline styles and attributes add “noise” to the signal. |
Contextual menu
Plain Markdown and/llms.txt help tools find content. The harder part is getting the page you are reading into an assistant or agent without fragile copy-paste. The contextual menu at the top of each page is where you turn the current doc into grounding context: copy text, open Markdown, or connect to assistants and editor tooling.