How to Check AI Generated Case Summaries

A good summary saves hours. A wrong one costs far more, because it becomes the version of the matter everyone works from without going back to the record. AI summaries are particularly seductive here: they are fluent, well-organised, and confident, which makes it easy to forget that the model compressed a contested record into tidy prose and made judgment calls you never saw.

The two failures to watch for are flattening and elevation. Flattening turns a live dispute into a settled narrative. Elevation turns an allegation into an established fact. Both read perfectly on the page.

Sort every statement into one of four buckets

Do not read the summary top to bottom looking for errors. Instead, go through it and assign each statement to a category:

• Agreed facts — matters both sides accept.
• Disputed assertions — one party's version of a contested point.
• Procedural events — what has actually happened in the proceeding.
• Open questions — things not yet established either way.

The value is in what this exercise exposes. A summary that reads smoothly often collapses these categories, presenting a claimant's allegation in the same neutral tone as an admitted fact. Once you have sorted the sentences, the ones the model quietly promoted from "alleged" to "is" stand out.

Check the verbs

Elevation usually lives in a single word. "The defendant failed to pay" states a fact; "the claimant alleges the defendant failed to pay" states a claim. Models drop the framing verbs — "alleges", "contends", "claims", "is said to" — because they make prose read more cleanly. Scan the summary specifically for factual verbs attached to contested events and restore the framing the record actually supports.

Sample against the source

You cannot re-read the whole file — that would defeat the point of a summary — but you can spot-check. Pick the summary's most consequential claims and trace each back to the underlying document or pleading. Confirm dates, amounts, and names, which models transcribe imperfectly, and confirm that the document says what the summary reports. If the samples hold up, your confidence in the rest rises; if two of five are off, the summary needs a full pass, not a trim.

Look for what is missing

Summaries fail by omission as much as by error. A defence not mentioned, a document that cuts against the headline narrative, a procedural step still outstanding — none of these show up as mistakes because they simply are not there. Read the summary once more asking not "is this right?" but "what would I need to know that this does not tell me?" This is the hardest check and the most valuable, because the model cannot flag the absence of something it left out.

Mark it as checked, and how

Once reviewed, note what the summary is: which parts were verified against the source, which remain the model's uncorrected output, and the date. A summary that travels through a matter without this marker gets treated as gospel by the next reader. One that carries "facts confirmed against pleadings; damages figures not yet checked" tells everyone exactly how far to trust it.

Used with this discipline, AI summaries are genuinely useful for getting oriented in an unfamiliar file quickly. The danger is never the summary itself; it is the summary read as the record. Keeping the summary anchored to the documents it came from — as it stays in a workspace like Juniorlawyer — means the source is always one step away when a statement needs to be checked, which is what keeps a helpful draft from hardening into an unexamined fact.