The Hallucination Problem in Legal Context
AI hallucination is especially dangerous in legal documents. Understanding why it happens makes you a better AI PM — and helps you explain BoostDraft's strategy in interviews.
What is hallucination and why does it happen
An AI hallucination is when a language model generates output that sounds confident and plausible but is factually incorrect or fabricated.
LLMs hallucinate because they're trained to predict the next token based on statistical patterns — not to retrieve facts. They learn to produce text that *sounds like* the training distribution, which means they can produce grammatically perfect, legally-formatted nonsense.
Example: Ask an LLM to summarize a contract's termination clause. It might return: 'The contract may be terminated by either party with 30 days written notice.' The real clause says 90 days. The LLM averaged across similar contracts it was trained on.
In Go, atari means your group is one move away from being captured. A hallucinating AI puts the user in atari: one accepted suggestion away from a serious legal error. The PM's job is to design features that keep the user out of atari.
The specific risks in legal documents
Legal documents are uniquely vulnerable to hallucination because:
1. Precision matters absolutely — 'shall not exceed $5 million' vs. 'shall not exceed $50 million' is a catastrophic difference, but both are plausible tokens after the preceding text.
2. Confidentiality is default — legal teams can't share real contracts for training data, which means LLMs are often trained on synthetic or public contracts that don't reflect the nuances of the actual documents being drafted.
3. Liability transfers — if a lawyer accepts an AI suggestion that contains an error, who is responsible? The lawyer, BoostDraft, or the LLM provider? This is an unresolved legal question that makes enterprise buyers very cautious.
4. Defined terms are local — in any given contract, 'the Company' means a specific entity. An LLM might confuse it with another party based on training patterns.
How BoostDraft's NLP approach sidesteps this
BoostDraft's rule-based and statistical NLP approach is grounded in the document itself. It doesn't generate new content — it extracts and cross-references what's already there.
When BoostDraft flags an inconsistency in a defined term, it can show exactly *where* the inconsistency is and *what* the conflicting text says. There's nothing invented — the evidence is in the document.
This is the PM angle: the absence of hallucination is a feature, not a limitation. It allows BoostDraft to make a reliability guarantee that no LLM-based legal tool can match.