Every legal AI vendor talks about hallucination. They say they reduce it, prevent it, or catch it before it reaches you. Some claim to be “bias-tested,” while others point to a low “error rate.” These terms get used interchangeably, but they describe different problems with different causes and different fixes.

If you can’t tell them apart, you can’t ask the right questions when evaluating a vendor, and you can’t accurately assess where your legal team’s real risk sits. A fabricated citation that lands in a filing is a malpractice and sanctions risk. A mischaracterized holding or a misattributed quote can be just as damaging, and much harder to spot.

This guide breaks down the real causes of AI errors in legal work and gives you practical ways to catch each one before it goes anywhere near a client or the court. You’ll also see how LOIS builds verification directly into your research, flagging every case as verified or unverified so you know what needs a second look.

Inside the guide:

• A clear breakdown of the five types of legal AI errors: fabricated cases, mischaracterized holdings, misattributed or false quotes, errors from submissions or exhibits, and bias

• Why each error happens, and why treating “hallucination” as one solvable problem leaves legal teams exposed

• Practical steps to verify AI output before it reaches a filing

• Sharper questions to ask when evaluating legal AI vendors

• How LOIS runs every case reference through a secondary verification layer, with a direct link to the real case when it’s verified

• Perspective from legal technology researcher Damien Charlotin on what AI hallucinations reveal about long-standing citation habits in the profession

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