# A4

### Overview

The A4 tokenization model allows for the creation of text-based tokens in the A4 output format.

* [Specifications](/tokenization-models/a4/specifications.md)

***

### Key Features

#### Multiple Pages

The A4 model is designed to consolidate multiple outputs into a single text-based token, incorporating a built-in pagination system. As a result, A4 enables the tokenization of documents with multiple pages.

***

### Intended Use Cases

The A4 tokenization model is designed for tokenizing long text records (e.g., documents). It is intended for use cases where a document requires the highest levels of immutability, permanency, attestability, and auditability of text records. This is particularly applicable in critical contexts that demand the utmost data provenance and integrity.

***

#### Legal

A4 can be used for tokenizing traditional critical legal documents, such as contracts, financial statements, title deeds, and patents.

<figure><img src="/files/5dVQSb8yXuTZ8eiG3w58" alt=""><figcaption></figcaption></figure>

***

#### AI

A4 can be integrated into AI-related frameworks, enabling the permanent recording of long text records generated during AI processes. This ensures unparalleled data provenance and integrity — from tokenizing AI model performance reports, explanation documents, and versioning, to tokenizing compliance audits, algorithmic intellectual property (IP), and data provenance and lineage.

<figure><img src="/files/nIQjvMI57tb47SIPUmk4" alt=""><figcaption></figcaption></figure>

***

#### Literature

A4 can be used for tokenizing critical literature documents, such as research articles, academic papers, technical whitepapers, and manifestos.

<figure><img src="/files/KbUSSaeOU69eYXTFFQdk" alt=""><figcaption></figcaption></figure>


---

# Agent Instructions: Querying This Documentation

If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter:

```
GET https://docs.xyxyx.pro/tokenization-models/a4.md?ask=<question>
```

The question should be specific, self-contained, and written in natural language.
The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.