When applying AI to documents, the result needs to be assessed against the task’s requirements. The first decision is what should be extracted and how it will be checked.
Define the task
Extracting fields, classifying documents and summarizing content are distinct tasks. Start with one and describe the required output.
For extraction, this might mean a table of fields. For classification, it could be a list of categories with defined criteria. The clearer the expected result, the more objective the assessment can be.
Prepare examples for checking
Gather a sample of documents that represents the intended use, including different formats and incomplete cases. Use only materials you are authorized to process.
For each example, record the expected result or the criterion a person would use to check the answer. This creates a reference for comparing the solution’s behavior.
Decide what happens when information is missing
A missing field should not be filled in by assumption. Define how the output represents missing information, uncertainty or the need for review.
The review should account for the importance of each field. An error in information used to make a decision may require different handling from an error in a purely descriptive detail.
Keep the connection to the document
Where it makes sense, the solution should allow the source of the information to be consulted. This makes checking and investigating discrepancies easier.
The interface and workflow matter too: reviewers need to understand what was extracted and how to correct a result.
Assess the whole process
Testing does not end with the model’s response. Consider how documents are received, how failures are handled, the review process and how the data is passed to the next system or the person responsible.
AI is one part of the solution. Its use should address a specific need, with defined validation criteria and limits.