Lesson 267 of 1550
AI-Assisted eDiscovery Search Strategy: Beyond Keyword Lists
Keyword search misses semantically related documents. AI-assisted concept searching catches documents traditional approaches miss — when paired with traditional methods.
Lesson map
What this lesson covers
Learning path
The main moves in order
- 1The premise
- 2eDiscovery
- 3search strategy
- 4concept search
Concept cluster
Terms to connect while reading
Section 1
The premise
Pure keyword search misses concept-related documents; AI-assisted concept search catches them, but defensibility requires layered methodology.
What AI does well here
- Layer keyword search (precision) with concept/semantic search (recall) for comprehensive coverage
- Document the search methodology before deployment for defensibility
- Sample-and-validate — calculate precision and recall on a known test set
- Maintain reviewer feedback loop where missed-document examples improve future searches
What AI cannot do
- Trust AI to find privileged documents (use specialized privilege workflow)
- Substitute for the meet-and-confer disclosure process
- Eliminate the cost of human review for high-stakes documents
Key terms in this lesson
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