Start with the legal task, classify confidentiality risk, choose the right category of tool, prepare documents, test on a limited matter, verify outputs and adopt a policy before wider deployment.
How to turn pleadings, correspondence and evidence into a source-controlled chronology while preserving page references and unresolved factual issues.
Compare official sources, commercial databases, citators and AI layers by authority coverage, currency, citation traceability and workflow fit.
Plain-English explanation of deployment labels and their practical consequences for confidentiality, cost, control and support.
What lawyers should check before using cloud, desktop or self-hosted PDF/OCR tools for confidential documents.
A staged process for extracting commercial instructions, reviewing clauses, identifying risks and preparing drafting instructions or a redraft.
How to define permitted tools, information classes, supervision duties, verification requirements, record-keeping and escalation routes.
Questions to ask about source coverage, data retention, model training, security, audit logs, export, support and contractual terms.
Short practitioner definitions of LLMs, RAG, hallucination, grounding, OCR, API, cloud, self-hosted, zero retention, tenant, encryption and human-in-the-loop.
Practical workflow for scanning, OCR, file naming, document indexing, quality checks and source control before AI-assisted review.
A supervised workflow for reviewing pleadings with AI while separating allegations, admissions, evidence gaps, authorities and lawyer judgment.
Checklist for verifying authorities, statutory currency, citations, binding status and unsupported propositions in AI-generated research.