The premise
LLM-based review is dramatically faster than TAR 1.0 — but defensibility requires the same methodology rigor courts have always demanded.
What AI does well here
- Establish a documented review protocol before deployment (not after a Rule 26 challenge)
- Maintain a control set of human-coded documents to validate AI accuracy
- Run accuracy testing per responsiveness category, not just overall
- Document privilege identification methodology separately — courts scrutinize this most
What AI cannot do
- Substitute for attorney review of privileged documents (privilege determinations remain attorney work)
- Make the responsiveness call on close-call documents
- Replace the meet-and-confer process where TAR methodology is disclosed
End-of-lesson check
10 questions · take it digitally for instant feedback at tendril.neural-forge.io/learn/quiz/end-legal-AI-discovery-document-review-adults
What is the main idea of "AI-Assisted Document Review for Discovery: TAR 2.0 and Beyond"?
- Technology-Assisted Review (TAR) has been around for a decade. Modern LLMs change the game — but courts still expect defensible methodology.
- Use AI as the final authority for the whole decision
- Avoid checking the answer once it sounds polished
- Focus only on speed instead of judgment
Which concept is most central to "AI-Assisted Document Review for Discovery: TAR 2.0 and Beyond"?
- predictive coding
- TAR
- discovery
- responsiveness
Which use of AI fits this topic best?
- Substitute for attorney review of privileged documents (privilege determinations remain attorney work)
- Let the AI decide what matters without your review
- Establish a documented review protocol before deployment (not after a Rule 26 challenge)
- Use the answer before checking whether it fits the situation
Which limitation should you watch for in this topic?
- Establish a documented review protocol before deployment (not after a Rule 26 challenge)
- Explain the topic in plain language
- Organize a draft for human review
- Substitute for attorney review of privileged documents (privilege determinations remain attorney work)
What should a careful learner remember about "Defensible AI review protocol"?
- Use "Defensible AI review protocol" as a reminder to verify the AI output before anyone relies on it.
- Skip the context so the tool can guess faster
- Treat the output as private even after sharing it online
- Use the answer without checking the source
You want to use AI after this lesson. What is the safest next step?
- Act immediately because the AI answer is written clearly
- AI cannot replace a licensed attorney or official legal/compliance source.
- Hide uncertainty so the final answer looks cleaner
- Use private or sensitive details before checking permission
How should AI output about TAR be treated?
- As proof that no other source is needed
- As a replacement for context, consent, or expert review
- As a draft or helper output that still needs human judgment and verification
- As something that becomes correct when it sounds confident
Name one way to verify an AI answer about TAR.
Which action would help you apply "AI-Assisted Document Review for Discovery: TAR 2.0 and Beyond" responsibly?
- Make the responsiveness call on close-call documents
- Use the tool to avoid thinking through the tradeoff
- Keep going even if the output conflicts with a trusted source
- Maintain a control set of human-coded documents to validate AI accuracy
Which choice is a bad use of AI for this lesson?
- Make the responsiveness call on close-call documents
- Establish a documented review protocol before deployment (not after a Rule 26 challenge)
- Ask for a plain-language explanation of predictive coding
- Compare the answer with a trusted source
