beginner
Legal AI Testing Checklist
A comprehensive pre-deployment checklist for validating AI outputs before they reach clients. Covers accuracy, hallucinations, edge cases, and professional standards.
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Methodology
A method for testing legal AI prompts, checking what they produce and managing context in large document reviews. It borrows from software testing and is adapted for legal work.
Why a Method Matters
Most lawyers use AI tools ad-hoc—asking questions, generating drafts, hoping for good results. This works until it doesn't. When stakes are high, you need a systematic check, not a hunch.
Repeatable Quality
Aim for consistent results across team members and use cases
Catch Failures Early
Look for invented content, errors and edge cases before they reach clients
Build Trust
Show clients (and partners) exactly how you checked AI outputs
The Method, Part by Part
beginner
Tracer Bullets: Test Simple Before Complex
Start with the simplest possible version of your legal task, validate it works, then progressively add complexity. Adapted from software engineering test-driven development.
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intermediate
Context Architecture: The 60% Rule
How to structure prompts and documents to stay within AI context window limits while maintaining quality. Includes progressive disclosure patterns for large contract analysis.
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intermediate
Five Validation Layers
A defence-in-depth approach to legal AI quality assurance. Multiple independent validation layers catch different failure modes before outputs reach clients.
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advanced
The Staircase of Complexity: Systematic Improvement
Build your prompt library systematically by starting with simple use cases and progressively adding complexity. Create a repeatable process for continuous improvement based on real failure data.
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Need Help Implementing These?
The consulting service helps law firms set up prompt testing, train teams to check AI output, and put quality assurance in place.
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