The Future of Ai Infused Documents: The Intelligent Document format.

Intelligent documents represent a shift in how we interact with traditional forms of information storage like Word documents, PowerPoint presentations, and Excel sheets.

Traditionally, these documents are fairly static, presenting a snapshot of data or information that doesn't change unless manually updated. This static nature often poses challenges, especially in professional settings where documents are left with clients or colleagues. Without the author's input, these documents can sometimes be difficult to navigate or understand fully.

This post explores a potentially new way to solve this problem.

The evolution towards intelligent documents incorporates generative AI, language models, and prompt engineering, transforming static documents into dynamic, interactive systems. This approach leverages the power of Ai to infuse documents with a layer of intelligence, essentially turning them into responsive entities that can understand and react to user queries or inputs.

At the core of intelligent documents is the use of metadata driven by LLMs. This metadata acts as a set of instructions or prompt engineering that defines the context and meaning of the information contained within the document. It can specify how to access external data via APIs, outline the logic and reasoning behind the document's structure, and set boundaries for how and where interactions with the document should occur.

Imagine a Request for Proposal (RFP) process, traditionally managed through a cumbersome 100-page document. With intelligent documents, this RFP could be transformed into an interactive system that understands the nuances of the request and it's responses, potentially making the need for a person to guide the review process obsolete. This system could automatically adjust its responses based on the user's queries, pulling in relevant data or tailoring explanations to fit the context of the question.

The future of productivity tools like Microsoft Word, Excel, and others lies in this integration of traditional document formats with the dynamic capabilities of AI.

This unseen metadata layer will define the functionality of intelligent documents, allowing them to respond, adapt, and provide tailored information based on the user's needs. This represents a significant leap forward in making information more accessible, understandable, and interactive, forever changing how we think about and interact with documents.

So How can we build this today?

Yes, it possible with existing off the shelf technology and specifications. The technical implementation of intelligent documents involves embedding an AI layer that acts as an unseen prompt, guiding the document's interaction with the user and external data sources.

This AI layer is crucial for integrating real-time data into documents, such as current costs, stock information, or any other relevant data that needs to be up-to-date. Additionally, it outlines the operational parameters, logic, and reasoning within the document, setting clear boundaries and definitions for its functionality.

To structure and define the components of an intelligent document, a TOML (Tom's Obvious, Minimal Language) like interface and XML can be employed.

TOML is a configuration file format that's easy to read due to its clear semantics. Using this format allows for the clear definition of various parts of a document, such as API integrations, data sources, logic rules, and operational limitations.

Using the example of an RFP document, the TOML-like structure would include sections that define:

1. API Integrations: This section lists all external data sources the document can pull information from, including keys or authentication tokens required to access these sources. It would specify, for instance, APIs for retrieving current market prices, stock levels, or cost estimates relevant to the RFP.
2. Data Fields: Here, specific fields within the document are mapped to their data sources or logic. For example, a field for "Estimated Project Cost" could be linked to a formula that calculates this cost based on real-time data fetched from various APIs.
3. Logic and Reasoning: This part outlines the operational logic of the document, such as how data fetched from APIs should be processed or displayed. It might include conditions under which certain data is included in the document or how user queries are interpreted and responded to.
4. Operational Parameters and Limitations: Defines the boundaries within which the document operates. This could include user permissions, the scope of data that can be fetched, and any limitations on the document's interactivity. Taking it future, if you asked a questions like how would adding additional capabilities or requirements effect the cost, the system could intelligently respond with real up-to-date data, or just say no, that's not feasible.
5. User Interaction Rules: Describes how the document should respond to user interactions, such as queries or requests for more detailed information. This includes mapping out potential questions and configuring the document's responses based on the AI's understanding of the content.

By structuring the intelligent document using a TOML-like interface, developers and content creators can precisely define how the document should behave, ensuring that users have a consistent and informative interaction with the document. This approach not only enhances the functionality of traditional documents but also makes them more adaptable and responsive to the user's needs.

What's it look like?

Below is an example of how a TOML format might look for an intelligent RFP document. This configuration outlines API integrations, data fields, logic and reasoning, operational parameters, and user interaction rules. It's worth noting that this configuration could be encrypted to remain unseen to the end user, ensuring that the operational logic and data integrations are secure and tamper-proof.

[API_Integrations]
# Defines APIs for real-time data retrieval
stockAPI = { url = "https://api.stockinfo.com", token = "secretToken" }
costAPI = { url = "https://api.costestimator.com", token = "secretToken" }

[Data_Fields]
# Maps document fields to data sources
"Estimated Project Cost" = { source = "costAPI", formula = "baseCost + (hours * hourlyRate)" }
"Stock Levels" = { source = "stockAPI", parameter = "productID" }

[Logic_and_Reasoning]
# Operational logic for data processing
"Cost Calculation" = { condition = "if projectScope = 'large', multiply baseCost by 1.2" }
"Stock Alert" = { condition = "if stockLevels < minimumStock, display = 'Reorder Required'" }

[Operational_Parameters]
# Document operational boundaries
userPermissions = "read-only"
dataScope = "currentFinancialQuarter"
interactivityLimitations = "queries limited to document scope"

[User_Interaction_Rules]
# Defines how the document interacts with user queries
queryMappings = [
  { query = "Explain the cost estimate", responseField = "Estimated Project Cost" },
  { query = "Check stock for product X", responseField = "Stock Levels" }
]        

This TOML format serves as a blueprint for creating an intelligent document, detailing every aspect of its functionality from data integration to user interactions.

Encrypting this configuration ensures that while the document can dynamically respond to user needs and external data changes, its core logic remains hidden and protected.

A more in-depth version could use my prompt engine TOML structure.

How to implement Intelligent Documents?

To implement the idea of intelligent documents using Microsoft's metadata layer, you can leverage the built-in and custom metadata capabilities of Microsoft Office documents. This approach allows for a seamless integration of dynamic content and logic within traditional document formats such as Word, Excel, and PowerPoint. By utilizing custom XML parts and the Document Information Panel, developers can store structured data and metadata within the document, enabling complex interactions based on the document's content and user inputs.

For the example of an RFP document mentioned earlier, let's incorporate the use of Microsoft's metadata layer with the TOML configuration for the five key parts: API Integrations, Data Fields, Logic and Reasoning, Operational Parameters, and User Interaction Rules. Here's how it could be structured:

1. API Integrations and Data Fields: Use custom XML parts to store API endpoint URLs, authentication tokens, and mappings between document fields and data fetched from these APIs. This setup allows for real-time data integration, such as updating cost estimates or stock information directly within the RFP document.
2. Logic and Reasoning: Embed InfoPath forms or similar structures as custom XML parts to define the logic for processing API data, such as calculating total costs or determining stock levels based on the inputs from the APIs.
3. Operational Parameters: Utilize the Document Information Panel to set and manage operational parameters like user permissions, document scope, and interaction limitations. This can be done by extending the panel with custom forms that capture these parameters as metadata.
4. User Interaction Rules: Define interaction rules within custom XML parts or through extended Document Information Panel forms. These rules specify how the document responds to user queries, such as providing explanations for cost calculations or fetching additional details on request.

To integrate this setup with the earlier mentioned TOML-like configuration, you would map the TOML structure to the custom XML parts and metadata fields within the Office document.

Here's an illustrative snippet of how the TOML configuration could be adapted and stored as custom XML parts within a Word document for an RFP:

<CustomProperties>
  <API_Integrations>
    <API name="stockAPI" url="https://api.stockinfo.com" token="secretToken"/>
    <API name="costAPI" url="https://api.costestimator.com" token="secretToken"/>
  </API_Integrations>
  <Data_Fields>
    <Field name="Estimated Project Cost" source="costAPI" formula="baseCost + (hours * hourlyRate)"/>
    <Field name="Stock Levels" source="stockAPI" parameter="productID"/>
  </Data_Fields>
  <Logic_and_Reasoning>
    <Logic name="Cost Calculation" condition="if projectScope = 'large', multiply baseCost by 1.2"/>
    <Logic name="Stock Alert" condition="if stockLevels < minimumStock, display = 'Reorder Required'"/>
  </Logic_and_Reasoning>
  <Operational_Parameters userPermissions="read-only" dataScope="currentFinancialQuarter" interactivityLimitations="queries limited to document scope"/>
  <User_Interaction_Rules>
    <QueryMapping query="Explain the cost estimate" responseField="Estimated Project Cost"/>
    <QueryMapping query="Check stock for product X" responseField="Stock Levels"/>
  </User_Interaction_Rules>
</CustomProperties>        

This XML structure, once embedded into the document, provides a robust framework for intelligent document functionalities, leveraging Microsoft Office's capabilities for metadata and custom XML storage. It ensures that documents can dynamically respond based on structured logic and data, transforming static documents into interactive, intelligent systems.

The Future of Intelligent Documents

Intelligent documents represent a new way of interacting with Word, PowerPoint, and Excel files, transforming them from static snapshots of information into dynamic, responsive tools. This shift leverages AI to make documents that can understand and react to user inputs, using metadata to guide their functionality.

This approach allows documents to access real-time data, follow logical operations, and interact intelligently with users. For instance, an RFP could become an interactive system that adjusts its responses based on queries, eliminating the need for manual review.

Implementing this involves using Microsoft Office's metadata capabilities and a clear, structured format like TOML to define document behavior. This setup enables the creation of documents that not only respond to current data and user needs but also remain adaptable and secure.

In essence, intelligent documents bring a significant leap in how we use and interact with digital documents, promising more accessible, understandable, and interactive content.