AI-assisted practical guide. Examples are hypothetical; these are proposed editorial methods, not reported research results.
Testing a name-matching algorithm requires a dataset that challenges the system's ability to distinguish between identical strings and actual identity. Using AI to generate this set allows you to quickly build a controlled environment where you know exactly which records should match and which should remain separate. This process ensures your logic handles collisions and variations without relying on sensitive real-world data.
Generating Diverse Name Pairs
Start by prompting the AI to create a list of fictional individuals with specific attributes. To test the system thoroughly, suggest a mix of exact matches, slight misspellings, and common nicknames. You should request that the AI provide unique identifiers for each person, such as a fictional employee ID or date of birth, to establish the ground truth. This allows you to verify if the algorithm is over-relying on the name string alone or correctly incorporating secondary identifiers to differentiate between two people who happen to share a name.
Hypothetical example
Imagine the test designer explicitly defines three fictional records: R1 and R3 belong to person P1, while R2 belongs to person P2. R1 says Sarah Jenkins, R3 says S. Jenkins, and R2 also says Sarah Jenkins. Ask the model to preserve that supplied identity key while presenting the names as a matching challenge. The expected answer matches R1 with R3 and keeps R2 separate because the test's ground truth says so. Name similarity or a shared birth year alone would not prove identity in real data. A reviewer checks that the hidden answer key survives every variation of the exercise.
Validating the Test Set
Once the AI generates the list, manually review the entries to ensure the intended collisions exist. The most difficult case occurs when the AI accidentally gives two different people the same secondary identifier, which would create a false positive. Check that the unique IDs are truly unique and that the intended "same-name, different-person" pairs have distinct supporting data. The final check involves running your matching algorithm against this set and comparing the output to your known ground truth. If the system merges the two different Sarah Jenkins entries into one profile, you have identified a failure in your disambiguation logic that needs correction.