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Legal Domain Cookbook

Complete Working Example

from director_ai.core import CoherenceScorer, GroundTruthStore

store = GroundTruthStore()  # empty — populate with your KB
store.add("statute of limitations", "The statute of limitations for personal injury in California is 2 years.")
store.add("contract elements", "A contract requires offer, acceptance, consideration, and mutual assent.")

scorer = CoherenceScorer(threshold=0.30, ground_truth_store=store)

# Correct → approved
approved, score = scorer.review("California injury statute of limitations?",
    "The statute of limitations for personal injury in California is 2 years.")
print(f"Correct: approved={approved}, score={score.score:.2f}")

# Wrong → rejected
approved, score = scorer.review("California injury statute of limitations?",
    "There is no statute of limitations for personal injury in California.")
print(f"Wrong:   approved={approved}, score={score.score:.2f}")

Configuration

from director_ai import CoherenceScorer, VectorGroundTruthStore

store = VectorGroundTruthStore()
store.ingest([
    "The statute of limitations for personal injury in California is 2 years.",
    "Attorney-client privilege protects communications made for legal advice.",
    "A contract requires offer, acceptance, consideration, and mutual assent.",
])

scorer = CoherenceScorer(
    threshold=0.30,    # CoherenceScorer scores cluster 0.25–0.55; tune on your data
    soft_limit=0.35,
    use_nli=True,
    ground_truth_store=store,
)

Cost Savings

Metric Without Director-AI With Director-AI (threshold=0.30)
Hallucinated citation rate 12–19% (model-dependent) < 1% with contract KB
Lawyer review hours per 100 AI drafts 50 hrs 12 hrs (review flagged only)
Annual review cost (1,000 queries/day) ~$5.5M ~$1.3M

At $300/hr associate rate and 1,000 AI-assisted queries/day, reducing review burden by 76% saves ~$4.2M/year. A single prevented fabricated citation avoids potential sanctions, malpractice claims, and bar complaints.

Key Considerations

  • Tune thresholds on your data: CoherenceScorer outputs 0.25–0.55; start at 0.30 and adjust
  • Flag borderline scores: soft_limit=0.35 flags near-threshold responses for human review
  • Retrieval fallback: always cite sources rather than hallucinate
  • Audit trail: enable AuditLogger for compliance
from director_ai.core.audit import AuditLogger

logger = AuditLogger(path="/var/log/director-ai/legal")