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Course Outline
Day 1: Build the Foundation — Ingest, Search, Retrieve
Module 1: The Legal Engineer’s Landscape
- Learning objectives — understand the role, where AI fits within legal work, and the two critical risks that permeate all operations.
- Topics
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- The legal-engineer role and current market demand.
- AI integration areas: 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 — manage the complexities of legal data at scale.
- Topics
- Handling 1,400+ file types, email and PST files, scanned documents, load files (.dat/.opt); identifying critical embedded metadata.
- Text extraction (Tika), OCR, and deduplication strategies.
- Lab: FreeEed Ingestion — construct an ingestion pipeline processing a deliberately messy document set (emails/PST, scans, load files).
Module 3: Search and Retrieval — the Foundation
- Learning objectives — build the core eDiscovery primitive: the ability to find anything within everything.
- Topics — full-text search and indexing (Solr/Lucene); relevance scoring, metadata and date filtering; searching across OCR-processed content.
- Lab: eDiscovery Search — index a corpus and execute real eDiscovery-style searches, including within OCR-processed scans.
Module 4: RAG for Legal Documents — with Citations
- Learning objectives — implement RAG over legal documents that cites its sources accurately.
- Topics
- Why retrieval, not fine-tuning, is preferred for sensitive material — ensuring the model does not ingest proprietary documents.
- Chunking, embeddings, and crucially, citations / provenance.
- Summarization of multiple documents and email threads.
- Lab: Legal RAG with Citations — develop a RAG Q&A system over a document set that provides answers accompanied by source citations.
Day 2: Ensure Privacy, Defensibility, and Deployment Readiness
Module 5: Privacy, Privilege, and Local Serving — Avoiding the Privilege Trap
- Learning objectives — keep legal data local and provide certification of that status.
- Topics
- Data pathways when interacting with cloud-based AI services.
- Privilege waiver risks, duty of competence, and the spectrum of “private” (contractual vs. physical).
- The case of Morgan v. V2X 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 demonstrate, via monitoring, that no data has left the environment.
Module 6: Defensible AI Review
- Learning objectives — measure and document an AI review process to ensure it holds up in legal contexts.
- Topics
- Critical metrics for court admissibility: recall, elusion, precision, ground-truth validation; TAR (Technology-Assisted Review) / active learning.
- Transparency (rationale for coding decisions) and reproducibility — pinning the model version, fixing settings, and logging all actions.
- The “defensible case snapshot” enabling others to re-run the review later and replicate the results.
- Lab: Defensible Review — evaluate an AI review against a blind ground truth and produce a reproducibility package.
Module 7: Ship It — Workflow, Private Deployment, and Governance
- Learning objectives — assemble components into a workflow, deploy privately, and evaluate the system.
- Topics
- A multi-step legal workflow (ingest → search → summarize → review → produce) incorporating human-in-the-loop processes.
- Essentials for private/on-premises deployment (containerization; keeping data within the organization).
- Brief overview of AI governance for legal and scoring the system using SAIS-100 (the Elephant Scale Secure AI Score).
- Lab: Score and Package — connect 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 it for private deployment.
- Participants depart with a portfolio project that mirrors the actual work of a legal engineer.
Optional Day 3 / Advanced Modules (delivered as a 3rd day or modular series)
- Investigations: Entities, Relationships, and Timelines — extract people/organizations/dates, reconstruct email threads, build chronologies, map near-duplicates and document lineage. Lab: construct a timeline and entity/relationship visualization.
- Agentic and Multi-Step Legal Workflows (deep dive) — advanced 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 high volumes, regulated environments (CJIS, government, higher education), hardware sizing. Lab: containerize and scale a processing job across multiple workers.
- Governance and Compliance Deep-Dive — the AI regulation landscape (100+ US 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 in Python and basic APIs.
- Helpful: User-level familiarity with LLMs (no machine learning background required—we will build the necessary mental model).
- No prior legal background required — essential legal concepts are taught in context.
Audience
- Software and AI engineers transitioning into legal technology.
- Engineers at legal-tech companies seeking deeper domain expertise.
- Technically oriented legal, eDiscovery, and information governance professionals who prefer to build solutions rather than just purchase them.
- Anyone aiming for the role of “legal engineer” or “AI legal engineer.”
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
