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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, AI’s position in legal work, and two pervasive risks.
- Topics:
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- The legal-engineer role and current market demand.
- AI applications: eDiscovery, review, contracts, research, investigations; understanding the EDRM model clearly.
- Build vs. buy considerations.
- Two universal risks: confidentiality/privilege and defensibility.
Module 2: Legal Data Is Messy — Ingestion and Extraction
- Learning objectives—manage legal data realities at scale.
- Topics:
- Handling 1,400+ file types, email/PST archives, scanned paper, load files (.dat/.opt); understanding critical embedded metadata.
- Text extraction (Tika), OCR, and deduplication 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—construct the core eDiscovery primitive: find anything within everything.
- Topics—full-text search and indexing (Solr/Lucene); relevance, metadata, and date filtering; searching across OCR-processed content.
- Lab: eDiscovery Search — index a corpus and execute real eDiscovery-style searches, including within OCR’d scans.
Module 4: RAG for Legal Documents — with Citations
- Learning objectives—build RAG over legal documents that cites sources.
- Topics:
- Why retrieval, not fine-tuning, is preferred for sensitive material—the model never absorbs the documents directly.
- Chunking, embeddings, and crucially, citations/provenance.
- Multi-document and thread summarization.
- Lab: Legal RAG with Citations — build a RAG Q&A system over a document set that answers 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 certifiable.
- Topics:
- Data flow when interacting with cloud AI services.
- Privilege waiver, duty of competence, and the 'private' spectrum (contractual vs. physical).
- Morgan v. V2X precedent 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 via monitoring that no data exited the environment.
Module 6: Defensible AI Review
- Learning objectives—measure and document an AI review to ensure it withstands legal challenge.
- Topics:
- Court-admissible metrics: recall, elusion, precision, ground-truth validation; TAR/active learning.
- Transparency (why was this document coded this way?) and reproducibility—pin the model, fix settings, log everything.
- The 'defensible case snapshot' allowing a review to be re-run later with 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.
- Private/on-prem deployment essentials (containerization; keeping data on-site).
- AI governance for legal professionals 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)
- Build a private, defensible legal-AI application end-to-end—ingest a messy corpus, search it, answer questions using citations via a local model, measure defensible review metrics, and package 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/organizations/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-document synthesis, tool use, and guardrails as design principles. 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 education), hardware sizing. Lab: containerize and scale a processing job across workers.
- Governance and Compliance Deep-Dive — 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 Large Language Models (LLMs)—no ML background is required as we build the conceptual framework.
- No legal background required—necessary legal concepts are taught within context.
Audience
- Software and AI engineers transitioning into legal technology.
- Engineers at legal-tech companies needing deeper domain knowledge.
- Technically-minded legal, eDiscovery, or information governance professionals who prefer building over buying.
- Anyone aiming for 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
