Asymmetry of Verifiability: Why “Check” Beats “Create” in Legal AI

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“Any task that is possible to solve and easy to verify will be solved by AI.”

Jason Wei, Verifier’s Law

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Large‑language models make it almost trivial to spin up an “agent” that drafts a clause or marks‑up a contract. What separates a flashy demo from a production‑ready legal tool is verifiability - the confidence that every suggestion complies with your risk profile, playbook, and jurisdictional quirks.

Below is a framework you can reference (or adapt inside ContractKen) to move the real value from generation to verification.

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Why drafting is easy (now) but verification still hurts

Thanks to today’s large‑language models, generating a first‑cut draft or proposing redlines is almost effortless: the model completes patterns it has seen thousands of times, so producing workable language takes seconds and minimal human input. The bottleneck comes afterward, when every clause must be checked against playbooks, defined‑term consistency, cross‑references, and risk thresholds. Here the AI’s skills are spottier and its hallucination risk rises, forcing lawyers back into painstaking line‑by‑line review.

This imbalance: fast creation versus slow, error‑prone verification - defines the “asymmetry of verifiability” that modern legal‑AI tools must solve.

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Four ideas for rock‑solid verification in legal AI

We see some industry activity around point #4 but no comprehensive approaches. Next, lets see how ContractKen can help you here

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How to build your own in-house or law firm evals pipeline?

1. Gold corpus 50-100 anonymized contracts, fully annotated with the “right” mark‑up and risk labels.
2. Micro‑bench tests (Clause extraction F1 ≥ 0.95, Risk categorization accuracy ≥ 90 %, Redline explain‑and‑cite completeness score, etc.)
3. CI trigger Every prompt or model update must pass the suite before it ships to production (GitHub Actions/Azure DevOps).
4. Shadow mode New model runs silently beside the live one; differences over a threshold are sent to human reviewers for fast feedback.
5. Monthly public check‑in Publish headline metrics next to VLAIR or other open benchmarks. Transparent scores build client trust and keep the team honest.

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Finally, takeaways for legal innovators:

• AI or Agentic drafting & review is table stakes. Anyone can call an API; few can prove correctness.
• Codify standards first. If a rule isn’t machine‑readable, it won’t be machine‑verifiable.
• Automate criticism, not just generation. A second model + static checks is the fastest path to trust.
• Benchmark openly. Open source what you can but also use external studies like VLAIR.
• Expose uncertainty. Citations, confidence scores, and dashboards will let your lawyers finish verification in minutes, not hours.

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Ready to shift your team from “drafting” to “deciding”?

At ContractKen we’re building that verification layer, so your lawyers can spend billable hours on judgement instead of proofreading. If you’re experimenting with your own evals framework or want a deeper dive into our pipeline, let’s connect in the comments or DM.

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PS: Cover image credits goes to Jason Wei's blog (must read)

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