Building Actuarial Models with AI

Gaspatchio was designed from the outset with Large Language Models (LLMs) and AI-driven workflows in mind. Its core philosophy revolves around a Python-native, human-readable Domain Specific Language (DSL) that is inherently easy for both humans and AI to understand, generate, and manipulate.

Why Gaspatchio for AI-Assisted Modeling?

1. Python-Native Foundation: Unlike systems with proprietary languages or complex GUIs, Gaspatchio uses standard Python constructs. This means:

   • LLMs trained on vast amounts of Python code can readily understand and generate Gaspatchio model code.
   • You can leverage AI coding assistants like Cursor directly within your modeling workflow. For instance, you can ask Cursor to refactor a calculation, add new assumptions, or generate boilerplate code using plain English prompts, significantly speeding up development. Many of the models understand both python and core actuarial concepts, and this combination is a powerful one.
   • Integration with the broader Python data science and AI ecosystem (NumPy, SciPy, PyTorch, etc.) is seamless.

2. Clear and Explicit Syntax: The Gaspatchio DSL emphasizes clarity and explicit declaration of variables, calculations, and their dependencies. This structured approach:

   • Reduces ambiguity for LLMs, leading to more accurate code generation and analysis.
   • Makes it easier for AI tools to trace logic, identify relationships between model components, and assist with debugging.
   • Provides a solid foundation for automated documentation generation and model validation.

3. Designed for Tooling: The framework provides introspection capabilities and clear feedback mechanisms, making it suitable for interaction with automated tools and agents. This opens possibilities for more advanced AI applications, such as:

   • AI Agents for Reconciliation: Deploying agents to compare model outputs, identify discrepancies, and potentially suggest resolutions.
   • Intelligent Scenario Analysis: Using LLMs to generate creative or complex scenarios based on high-level descriptions or external data feeds.
   • Automated Model Auditing: Developing AI tools to review model code for potential errors, inconsistencies, or deviations from best practices.

Note

We're working on this tooling now, and using it to help us build Gaspatchio so that it's tuned for LLM use.

Watch this space! 👀

For a deeper dive into specific prompts and techniques for using LLMs with Gaspatchio, especially if you are an LLM interacting with this documentation, please refer to the dedicated resources at the root of the documentation:

• llms.txt: Provides concise, LLM-friendly context, guidance, and links to key documentation sections. This follows the emerging llms.txt convention (llmstxt.org), designed to give LLMs essential, structured information about a project or website efficiently, without needing to parse complex HTML. It's ideal for quick lookups and basic understanding.
• llms-full.txt: An expanded version, potentially including content from linked resources mentioned in llms.txt. This offers a more comprehensive context suitable for deeper analysis or more complex query answering, though it requires a larger context window.

Gaspatchio adopts this standard to make its documentation more accessible and useful for AI tools, facilitating automated tasks and improving the accuracy of AI-assisted development.