Q: What is the first step recommended in the Law library AI implementation guide for starting an AI program at a law library? A: The Law library AI implementation guide recommends forming a cross‑functional AI team that brings together librarians, IT, general counsel, privacy, accessibility, and teaching staff, with an assigned project lead and regular meetings. It also suggests setting six‑month outcomes, picking a small number of pilot use cases, and creating a shared decision log. Q: How should a law library write an AI policy and risk framework according to the Law library AI implementation guide? A: The Law library AI implementation guide advises keeping policy short, direct, and visible while setting clear guardrails such as requiring human review of all AI outputs. It recommends using tools that cite sources or allow document uploads, prohibiting unapproved uploads of confidential or student data, and documenting AI use in research notes and syllabi with verification of quotes and facts. Q: What practical use cases does the Law library AI implementation guide suggest starting with? A: The Law library AI implementation guide recommends beginning with low‑risk, high‑value tasks like research helpers (drafting search strategies and summarizing cases), teaching aids (writing partners and outline generators), document analysis, and simple operations such as service desk triage and scheduling. Starting with these areas lets libraries get quick wins while minimizing risk and complexity. Q: How does the Law library AI implementation guide recommend vetting AI vendors for library use? A: The Law library AI implementation guide suggests using a structured scorecard that assesses evidence of accuracy, security and privacy practices, contract terms, product features like citation grounding and admin controls, and accessibility and DEI considerations. It also recommends requesting demos with sources, checking data retention and training‑use policies, and negotiating indemnity and audit rights in contracts. Q: What approach to pilots and tool comparisons does the Law library AI implementation guide recommend? A: The Law library AI implementation guide recommends running tight pilots that define specific tasks (for example, memo outlines, finding controlling authority, or summarizing a case), scoring outputs for accuracy, citation quality, explainability, time saved, and cost, and red‑teaming with edge cases. It further advises comparing tools side by side, keeping sample outputs, and using pilot results to inform teaching and operational workflows. Q: How does the Law library AI implementation guide recommend training students, faculty, and staff on AI? A: The Law library AI implementation guide emphasizes layered support that teaches skills rather than just tools, including foundational modules on prompts, evaluation, and bias, applied labs like building a writing partner or document analysis, and drop‑in help such as a Curiosity Corner. It notes that Stanford supplemented workshops with an AI Learning Hub and a one‑credit AI Literacy for Lawyers course to provide structured learning and materials for faculty integration. Q: What day‑to‑day verification and prompt practices does the Law library AI implementation guide recommend to reduce hallucinations and errors? A: The Law library AI implementation guide recommends simple verification habits such as asking for citations and checking each one, comparing AI summaries to the original text, and cross‑checking key points with a second model or a database search. It also advises designing prompts that provide source text and require the model to quote and cite only from it, set boundaries like “If unsure, say you don’t know,” and request confidence assessments and verification checklists. Q: How should a law library measure outcomes and scale AI initiatives according to the Law library AI implementation guide? A: The Law library AI implementation guide recommends tracking usage (who uses what and how often), quality metrics like hallucination and citation error rates, impact measures such as time saved and student learning gains, and costs versus value while monitoring compliance. It further advises publishing internal guides and sample assignments, standardizing on a small supported toolset, negotiating enterprise terms after successful pilots, and revisiting policy and training each semester.