AI Context & Configuration

Multi-layer documentation for an internal AI lending analytics assistant.

AI Context & Configuration for an Internal Lending Analytics Assistant

This repository demonstrates how to create AI context files, configuration documentation, and user guidance for an internal analytics assistant using synthetic lending data.
It is intended as a documentation and context-engineering example only.

ExampleCorp has an internal AI chatbot that answers questions about lending data and model performance.

Context greatly improves chatbots — but an internal AI analytics system needs more than one context layer to work correctly.

Why Multiple Context Layers Matter

Different context layers are needed for different situations:

• Data and model context so the AI system can correctly interpret tables, columns, and model outputs
• Configuration context so platform and BI teams can operate, restrict, and govern the AI analytics tool safely
• Usage instructions so internal users understand how to ask questions and interpret results

In this example, the chatbot relies on these three documentation layers to understand what the data means, how it is allowed to be used, and how its answers should be interpreted.

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Example Use Cases

Internal teams such as:

• BI analysts
• Product managers
• Risk / operations

May ask questions like:

• “What was the approval rate last week by product type?”
• “Which financial signals correlate most with decline?”
• “How did model v3 perform vs v2?”

To answer these reliably, the system depends on structured documentation.

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Documentation Layers

AI Context

Defines tables, columns, joins, constraints, and model inputs vs outputs.

AI Tool Configuration

Governance rules, masked fields, row-level restrictions, and join rules.

User Guidance

Instructions for internal teams on how to use and interpret the AI analytics tool.