Legal AI Engineering Training Course

Day 1: Build the Foundation — Ingest, Search, Retrieve

Module 1: The Legal Engineer’s Landscape

• Learning objectives — understand the role of a legal engineer, where AI fits into legal work, and the two critical risks that permeate the field.
• Topics
• • The legal-engineer role and current market demand.
  • AI applications: eDiscovery, review, contracts, research, investigations; the EDRM model explained simply.
  • Build vs. buy considerations.
  • The two pervasive risks: confidentiality/privilege and defensibility.

Module 2: Legal Data Is Messy — Ingestion and Extraction

• Learning objectives — handle the reality of processing legal data at scale.
• Topics
  • Handling 1,400+ file types, emails, PSTs, scanned papers, load files (.dat/.opt); understanding relevant embedded metadata.
  • Text extraction (Tika), OCR, and de-duplication strategies.
• Lab: FreeEed Ingestion — build an ingestion pipeline over a deliberately messy document set (email/PST, scans, load files).

Module 3: Search and Retrieval — the Foundation

• Learning objectives — build the core eDiscovery primitive: the ability to find anything inside everything.
• Topics — full-text search and indexing (Solr/Lucene); relevance, metadata, and date filtering; searching across OCR’d content.
• Lab: eDiscovery Search — index a corpus and run real eDiscovery-style searches, including within OCR’d scans.

Module 4: RAG for Legal Documents — with Citations

• Learning objectives — build a Retrieval-Augmented Generation (RAG) system over legal documents that cites its sources.
• Topics
  • Why retrieval, not fine-tuning, is preferred for sensitive material—the model never ingests the documents directly.
  • Chunking, embeddings, and critically, citations / provenance.
  • Multi-document and thread summarization.
• Lab: Legal RAG with Citations — build a RAG Q&A system over a document set that answers queries with source citations.

Day 2: Make It Private, Defensible, and Shippable

Module 5: Privacy, Privilege, and Local Serving — the Privilege Trap

• Learning objectives — keep legal data local and able to certify its security.
• Topics
  • What happens to data when it hits a cloud AI service.
  • Privilege waiver, duty of competence, and the spectrum of "private" (contractual vs. physical).
  • The precedent of Morgan v. V2X and why local solutions are court-defensible.
  • Serving local models (Ollama / vLLM) and monitoring outbound traffic.
• Lab: Local Model + Egress Proof — run a local model end-to-end and prove via monitoring that no data exited the environment.

Module 6: Defensible AI Review

• Learning objectives — measure and document an AI review process so it holds up in court.
• Topics
  • Critical metrics for court: recall, elusion, precision, ground-truth validation; TAR / active learning.
  • Transparency (why did it code this document?) and reproducibility — pin the model version, fix settings, log everything.
  • The "defensible case snapshot" that allows someone to re-run your review a year later and achieve identical results.
• Lab: Defensible Review — measure an AI review against a blind ground truth and produce a reproducibility bundle.

Module 7: Ship It — Workflow, Private Deployment, and Governance

• Learning objectives — assemble components into a workflow, deploy privately, and score the system.
• Topics
  • A multi-step legal workflow (ingest → search → summarize → review → produce) with human-in-the-loop oversight.
  • Essentials for private/on-premises deployment (containerization; keeping data on-site).
  • Brief AI governance for legal contexts and scoring the system using SAIS-100 (the Elephant Scale Secure AI Score).
• Lab: Score and Package — wire a multi-step workflow, score it with SAIS-100, and package it for private deployment.

Capstone (integrated across Day 2)

• Construct a private, defensible legal-AI application end-to-end — ingest a messy corpus, search it, answer questions with citations using a local model, measure a defensible review, and package for private deployment.
• Participants leave with a portfolio project that mirrors the responsibilities of a legal engineer role.

Optional Day 3 / Advanced Modules (deliverable as a 3rd day or a modular series)

• Investigations: Entities, Relationships, and Timelines — extract people/orgs/dates, reconstruct email threads, build chronologies, map near-duplicates and document lineage. Lab: build a timeline and entity/relationship view.
• Agentic and Multi-Step Legal Workflows (deep dive) — richer orchestration, contract analysis, multi-document synthesis, tool use, and guardrails as a design principle. Lab: build a multi-step workflow with a human checkpoint.
• Deployment at Scale — on-premises and appliance deployment, distributed processing for large volumes, regulated environments (CJIS, government, higher-ed), hardware sizing. Lab: containerize and scale a processing job across workers.
• Governance and Compliance Deep-Dive — the AI-regulation landscape (100+ US state AI laws, the EU AI Act), audit requirements, and a full SAIS-100 governance audit. Lab: audit a legal-AI system against a governance/defensibility checklist.