Structured Outputs for AI-Generated Financial Models: Schemas Before Spreadsheets

<report 5a09dfb2-239>Structured Outputs for AI-Generated Financial Models: Schemas Before Spreadsheets</report> The article emphasizes that generating a financial model with an LLM is not primarily about spreadsheet generation, but rather a data-contract problem. It suggests that a more reliable approach is to represent the model as structured data, validate that structure, perform deterministic calculations, and only then render the result into a spreadsheet.

The author provides an example of a simple development model with inputs such as gross floor area, saleable area, selling price, construction cost, professional fees, financing assumptions, and development timing. They illustrate how a prompt like "Create an Excel development feasibility model from these assumptions" may produce a workbook that looks reasonable, but does not necessarily ensure the model is correct.

The article stresses the importance of explicitly representing modelling decisions, such as the use of gross floor area or saleable area in calculating construction cost, and the distinction between user inputs and derived values. To address these issues, the author proposes starting with a structured model specification, which can describe the financial model without depending on a particular spreadsheet.

This representation can be validated before any spreadsheet is generated and can determine whether calculations reference known inputs and whether units and expected data types are consistent. The article concludes by highlighting the usefulness of JSON Schema in defining the expected structure of the data, requiring that value is a number, unit is a string, and source_type belongs to an approved set of values.

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