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, where AI fits in legal work, and the two risks that permeate everything.
• Topics
• • The legal-engineer role and why it is currently in high demand
  • Where AI fits: eDiscovery, review, contracts, research, investigations; explaining the EDRM model in plain terms
  • 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 managing legal data at scale.
• Topics
  • Handling 1,400+ file types, email/PST files, scanned paper, load files (.dat/.opt); understanding critical 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: finding any information within any dataset.
• Topics — full-text search and indexing (Solr/Lucene); relevance scoring, metadata and date filtering; searching across OCR’d content
• Lab: eDiscovery Search — index a corpus and run real eDiscovery-style searches, including inside OCR’d scans

Module 4: RAG for Legal Documents — with Citations

• Learning objectives — build RAG over legal documents that cites its sources.
• Topics
  • Why retrieval, not fine-tuning, is preferred for sensitive material — the model never ingests the raw documents
  • Chunking, embeddings, and above all citations / provenance
  • Multi-document and thread summarization techniques
• 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 be able to certify its security.
• Topics
  • Where data actually goes when interacting with cloud AI services
  • Privilege waiver, duty of competence, and the “private” spectrum (contractual vs. physical controls)
  • Morgan v. V2X case study and why local deployment is 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, with monitoring tools, that no data left the environment

Module 6: Defensible AI Review

• Learning objectives — measure and document an AI review process so it withstands scrutiny.
• Topics
  • The metrics that hold up in court: recall, elusion, precision, ground-truth validation; TAR / active learning concepts
  • Transparency (understanding why a document was coded a certain way) and reproducibility — pinning the model version, fixing settings, logging all actions
  • The “defensible case snapshot” allowing 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 it privately, and evaluate its quality.
• Topics
  • A multi-step legal workflow (ingest → search → summarize → review → produce) incorporating human-in-the-loop processes
  • Private/on-prem deployment essentials (containerization; keeping data within the organization)
  • AI governance for legal sectors and scoring systems 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)

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

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) — advanced orchestration, contract analysis, multi-doc 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.