Legal AI Engineering Training Course

Day 1: Establishing Operational Foundations — Data Ingestion, Search, and Retrieval

Module 1: Overview of the Legal Engineering Domain

• Learning objectives — comprehend the evolving role of the legal engineer, the integration of AI within legal operations, and two critical risk factors inherent to all legal technology deployments.
• Topics
  • The emerging demand for legal engineering roles and current hiring trends
  • AI application domains: eDiscovery, document review, contract lifecycle management, legal research, and investigations; conceptualizing the Electronic Discovery Reference Model (EDRM)
  • Strategic considerations for build versus buy decisions
  • Primary risk vectors: maintaining confidentiality/privilege and ensuring defensibility in court proceedings

Module 2: Managing Complex Legal Data — Ingestion and Extraction Protocols

• Learning objectives — address the complexities of processing large-scale legal data sets.
• Topics
  • Handling diverse formats (1,400+ file types), email archives (.pst), scanned documents, and load files (.dat/.opt); importance of embedded metadata
  • Techniques for text extraction (Tika), Optical Character Recognition (OCR), and de-duplication strategies
• Practical Exercise: FreeEed Ingestion Pipeline — construct an ingestion pipeline designed to process a deliberately unstructured document set comprising emails, PST files, scans, and load files.

Module 3: Search and Retrieval Infrastructure

• Learning objectives — establish the core eDiscovery capability: comprehensive searching across all stored information.
• Topics — Full-text search indexing (Solr/Lucene); managing relevance, metadata filters, and date ranges; conducting searches within OCR-processed content.
• Practical Exercise: eDiscovery Search Implementation — index a data corpus and execute realistic eDiscovery queries, including searches within scanned images.

Module 4: Retrieval-Augmented Generation (RAG) for Legal Documents with Source Attribution

• Learning objectives — implement RAG systems for legal documents that provide verifiable source citations.
• Topics
  • Rationale for using retrieval over fine-tuning when handling sensitive data: preserving model neutrality and preventing data leakage
  • Data chunking, embedding vectors, and the critical importance of citations and provenance
  • Techniques for summarizing multiple documents and email threads
• Practical Exercise: Legal RAG with Citations — develop a RAG-based query system that answers questions using source citations from the document set.

Day 2: Ensuring Privacy, Defensibility, and Deployment Readiness

Module 5: Privacy, Privilege, and Local Model Deployment

• Learning objectives — maintain data sovereignty and achieve certification for legal data processing.
• Topics
  • Data flow risks associated with cloud-based AI services
  • Risks of privilege waiver, duties of competence, and the spectrum of privacy (contractual vs. physical isolation)
  • Legal precedents such as Morgan v. V2X underscoring the defensibility of local processing
  • Deploying local models (Ollama/vLLM) and monitoring outbound network traffic to prevent data egress
• Practical Exercise: Local Model and Egress Verification — execute a local model workflow end-to-end and demonstrate via monitoring tools that no data left the secure environment.

Module 6: Defensible AI-Assisted Review

• Learning objectives — quantify and document AI review processes to ensure admissibility in legal proceedings.
• Topics
  • Critical metrics for court acceptance: recall, elusion, precision, and ground-truth validation; Technology Assisted Review (TAR) and active learning
  • The necessity of transparency (explaining coding decisions) and reproducibility — locking model versions, configuring fixed settings, and maintaining comprehensive logs
  • Creating a "defensible case snapshot" to allow for consistent re-execution of review processes years later
• Practical Exercise: Defensible Review Measurement — evaluate an AI review against blind ground truth data and generate a reproducibility package.

Module 7: Deployment Strategy — Workflow Integration, Private Hosting, and Governance

• Learning objectives — assemble components into a cohesive workflow, deploy securely in private environments, and assess system performance.
• Topics
  • Designing multi-step legal workflows (ingest → search → summarize → review → produce) with human-in-the-loop oversight
  • Essentials for on-premise/private deployment: containerization strategies and data residency controls
  • AI governance frameworks for legal sectors and system scoring using the SAIS-100 "Elephant Scale" Secure AI Score
• Practical Exercise: System Scoring and Packaging — connect multi-step workflow components, evaluate using SAIS-100 criteria, and package the solution for secure deployment.

Capstone Project (Integrated across Day 2)

• Develop a private, defensible legal AI application end-to-end — ingest unstructured data, perform search, answer queries with citations using local models, validate review defensibility, and package for secure deployment.
• Participants will complete a portfolio-ready project mirroring the responsibilities of a legal engineer.

Optional Day 3 / Advanced Modules (Available as a standalone day or modular series)

• Investigative Analysis: Entities, Relationships, and Timelines — extract people, organizations, and dates; reconstruct email threads; build chronologies; and map near-duplicates and document lineage. Lab: Construct a timeline and entity-relationship graph.
• Advanced Agentic and Multi-Step Legal Workflows — enhanced orchestration, contract analysis, multi-document synthesis, tool integration, and guardrails as design principles. Lab: Build a multi-step workflow with human checkpoint approval.
• Large-Scale Deployment Strategies — on-premise and appliance deployment, distributed processing for high-volume data, compliance in regulated environments (CJIS, government agencies, higher education), and hardware capacity planning. Lab: Containerize and scale a processing job across multiple workers.
• Governance and Compliance Deep Dive — overview of the AI regulatory landscape (including 100+ U.S. state laws and the EU AI Act), audit requirements, and a comprehensive SAIS-100 governance audit. Lab: Audit a legal AI system against a defensibility checklist.