Evaluating Invoice Data with LLM as a Judge

In recent years, AI systems have become more autonomous and complex. However, an important question arises: how do we evaluate whether they are performing their tasks correctly? This is particularly relevant for AI pipelines that process supplier invoices and extract key fields such as Invoice ID, Total Amount, and Supplier Name.

The data extraction process can be intricate, and manually checking thousands of documents is inefficient. This is where the concept of LLM as a Judge comes into play. Instead of writing fragile validation logic, we can utilize a language model to evaluate the extracted data by comparing it to verified values.

What is LLM as a Judge?

This evaluation method uses a large language model not to perform the primary task but to assess the output of another model. This concept has gained popularity in production AI systems due to its scalability and flexibility. It allows for processing thousands of records without human involvement and provides explanations for each assessment.

Implementation Steps

• Initial Setup: Create a database and schema for this process.
• Create Tables: Three tables are needed: the extractions table, the ground truth table, and the results table.
• Insert Synthetic Data: Create synthetic invoice documents for evaluation.

The outcome is a closed-loop evaluation system where AI outputs are continuously measured and improved, which is crucial for embedding AI into enterprise workflows.