AInews · 2026-06-17 · 1 min read
Before changing the AI model, check your data
How to check inputs, define a useful result, and find the cause of unreliable AI output.
Before changing an AI model or rewriting a prompt, check the information the system receives. Missing records, conflicting values, and outdated instructions can make an otherwise useful tool produce unreliable work.
Start with the decision
Define what the team needs to decide, which information supports it, and what a correct result looks like. A forecasting task, a document check, and a customer reply may need different tools and different tests.
Check the inputs
- Identify the source and owner of each important field.
- Distinguish missing values from genuine zeros or empty answers.
- Check dates, units, duplicates, and conflicting records.
- Give the model relevant information rather than an unfiltered collection of files.
Test the complete workflow
Use examples from real work, including incomplete or unusual cases. Compare the output with an agreed answer or a reviewer's decision. When something fails, trace it back to the data, the rules, or the model before choosing a fix.
Planning an AI automation initiative? Read the AI automation POC guide.