How to verify an AI generated title abstract

Start with what the file actually contains

Before checking the answer, check the inputs. Which documents went into the file? Are all the pages readable? Does a deed refer to an exhibit that never arrived? An abstract cannot reliably summarize material that was never provided, even if the finished paragraph sounds perfectly confident.

Write down the assignment too. Extracting names and recording references is different from deciding what a document means for a transaction. Our guide to searching county records helps you document the research scope. Keeping the task clear makes it easier to spot a result that goes beyond the evidence.

You do not need a complicated review system to start. Keep a document list, a place for corrections, and a separate list of unanswered questions. The reviewer should be able to see those three things without opening every file again. Treat this as a practical review habit, not a complete examination standard.

Walk through a small example

Imagine a fictional training packet with a deed, a mortgage, and a document labeled release. These are invented records, not a real property or a demonstration of TITL accuracy. On deed page one, the buyer is Morgan Lee. Page two says the property description is in Exhibit A, but that exhibit is missing.

The AI summary says Morgan Lee acquired Lot 12 and that the mortgage has been released. The buyer's name has support on deed page one. Lot 12 does not have support in the supplied deed pages. The release also names a different mortgage recording number from the mortgage in this packet.

That gives the reviewer three different outcomes. Keep the buyer's name with its page reference. Mark the property description as unverified and request Exhibit A. Flag the proposed mortgage and release connection for review. The release label alone does not establish that it affects this mortgage.

The corrected note can be simple: buyer name checked against deed page one; property description awaiting missing exhibit; mortgage release relationship unresolved. That is less polished than a confident conclusion, but much more useful to the next person.

Check statements, not just clickable links

A source link is a starting point, not a pass mark. Open it and ask whether the page actually supports the statement beside it. Check names, dates, recording references, and property identifiers separately. A correct name does not make every other field correct.

Also separate copied text from interpretation. A system might read a name from a scan, standardize its spelling, or suggest that two names belong to the same person. Ask which happened. Keep the original text available so a reviewer can compare the suggestion with the record.

NIST's AI risk guidance provides a broader framework for evaluating AI systems. The specific checking routine here is our editorial suggestion, not a NIST approved title checklist. For a larger pilot, use our software evaluation guide to record what worked and what still needs testing.

Reference: NIST: AI Risk Management Framework

Make the handoff easy to trust

Keep the original output and the reviewed version together. Record who changed a field, why it changed, and which page supports the correction. If another document arrives later, the team should be able to tell what was known before it arrived.

Count review time when you evaluate the benefit. A summary that appears quickly but takes ages to check may not improve the full workflow. Compare that work with TITL's review workflow, and distinguish freed capacity from actual cash savings.

Before handing the file over, ask whether another reviewer can find the evidence, understand the limits, and identify the next action. If the answer is yes, the abstract is doing a useful job. It still is not a substitute for the professional judgment required for the actual transaction.