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Course Outline
Day 1: Establishing Core Competencies — Data Ingestion, Search, and Retrieval
Module 1: Defining the Legal Engineering Landscape
- Learning objectives — Comprehend the professional role, the integration of AI in legal operations, and the two critical risks inherent in all workflows.
- Topics
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- The role of the legal engineer and the current demand for these specialized skills
- AI applications: eDiscovery, document review, contract management, research, and investigations; an overview of the EDRM model
- Strategic decision-making: building in-house capabilities versus procuring external solutions
- Critical risk management: preserving confidentiality/privilege and ensuring procedural defensibility
Module 2: Managing Legal Data Complexity — Ingestion and Extraction
- Learning objectives — Manage the practical challenges of processing legal data at scale.
- Topics
- Handling diverse file formats (1,400+ types), email archives (PST), scanned documents, and load files (.dat/.opt); the significance of embedded metadata
- Text extraction techniques (Tika), Optical Character Recognition (OCR), and strategies for de-duplication
- Practical Exercise: FreeEed Ingestion — Develop an ingestion pipeline processing a complex document set comprising emails, PST files, scans, and load files
Module 3: Search and Retrieval — The Operational Foundation
- Learning objectives — Establish the core eDiscovery capability to locate specific information within comprehensive datasets.
- Topics — Full-text search and indexing (Solr/Lucene); relevance ranking, metadata filtering, and date restrictions; searching within OCR-processed content
- Practical Exercise: eDiscovery Search — Index a corpus and execute eDiscovery-style queries, including searches within OCR-processed scans
Module 4: Retrieval-Augmented Generation (RAG) for Legal Documents — with Citations
- Learning objectives — Implement RAG for legal documents that provides verifiable source citations.
- Topics
- Why retrieval, not fine-tuning, is preferred for sensitive material — ensuring the model does not retain document content
- Chunking, embeddings, and the critical importance of citations and provenance
- Summarization of multi-document sets and communication threads
- Practical Exercise: Legal RAG with Citations — Develop a RAG-based Q&A system over a document set that answers queries with explicit source citations
Day 2: Ensuring Privacy, Defensibility, and Operational Readiness
Module 5: Privacy, Privilege, and Local Serving — The Privilege Trap
- Learning objectives — Keep legal data local and be able to certify its security.
- Topics
- Tracing data flow when utilizing cloud-based AI services
- Privilege waiver risks, duty of competence, and the distinction between contractual and physical privacy controls
- Morgan v. V2X and the court-defensibility of local processing
- Serving local models (Ollama / vLLM) and monitoring outbound traffic
- Practical Exercise: Local Model + Egress Proof — Execute a local model end-to-end and demonstrate, through monitoring, that no data egressed
Module 6: Defensible AI Review
- Learning objectives — Measure and document an AI review to ensure legal validity.
- Topics
- Metrics recognized in court: recall, elusion, precision, and ground-truth validation; TAR / active learning approaches
- Transparency (explaining document coding decisions) and reproducibility — pinning model versions, fixing settings, and logging all actions
- Creating a “defensible case snapshot” that allows for consistent re-execution of reviews in the future
- Practical Exercise: Defensible Review — Evaluate an AI review against a blind ground truth and generate a reproducibility bundle
Module 7: Deployment — Workflow, Private Infrastructure, and Governance
- Learning objectives — Integrate components into a cohesive workflow, deploy privately, and assess performance.
- Topics
- Designing multi-step legal workflows (ingest → search → summarize → review → produce) with human oversight
- Essentials of private/on-premises deployment (containerization; ensuring data remains on-site)
- AI governance for legal sectors, and system scoring using SAIS-100 (the Elephant Scale Secure AI Score)
- Practical Exercise: Score and Package — Implement a multi-step workflow, assess it with SAIS-100, and package it for private deployment
Capstone Project (Integrated across Day 2)
- Develop a private, defensible legal-AI application end to end — ingest a complex corpus, search it, answer questions with citations using a local model, measure review defensibility, and package for private deployment.
- Participants will complete the course with a portfolio project that reflects the core competencies of a legal engineer role.
Optional Day 3 / Advanced Modules (Available as a third day or modular series)
- Investigations: Entities, Relationships, and Timelines — Extract people, organizations, and dates; reconstruct email threads; build chronologies; map near-duplicates and document lineage. Lab: construct a timeline and entity/relationship view.
- Agentic and Multi-Step Legal Workflows (Advanced) — Complex orchestration, contract analysis, multi-document synthesis, and tool usage with guardrails as a design principle. Lab: build a multi-step workflow with human checkpoints.
- Deployment at Scale — On-premises and appliance deployment, distributed processing for large volumes, compliance with regulated environments (CJIS, government, higher education), and hardware sizing. Lab: containerize and scale processing jobs across multiple workers.
- Governance and Compliance Deep-Dive — Analysis of the AI regulatory landscape (100+ U.S. state AI laws, EU AI Act), audit requirements, and a comprehensive SAIS-100 governance audit. Lab: audit a legal-AI system against a governance/defensibility checklist.
Requirements
- Proficiency with Python and basic APIs.
- Beneficial: Familiarity with LLMs at a user level (no ML background required — the course builds the necessary mental models).
- No legal background required — essential legal concepts are taught within context.
Target Audience
- Software and AI engineers transitioning into legal tech.
- Engineers at legal-tech companies who require depth in the legal domain.
- Technically proficient legal, eDiscovery, or information governance professionals who wish to build solutions rather than just procure them.
- Professionals targeting the “legal engineer” or “AI legal engineer” role.
14 Hours
Testimonials (1)
That i gained a knowledge regarding streamlit library from python and for sure i'll try to use it to improve applications in my team which are made in R shiny
