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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 in legal work, and the two risks that permeate all operations.
- Topics
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- The legal-engineer role and current market demand.
- Where AI fits: eDiscovery, review, contracts, research, investigations; the EDRM model explained simply.
- Build vs. buy decisions.
- The two omnipresent risks: confidentiality/privilege and defensibility.
Module 2: Legal Data Is Messy — Ingestion and Extraction
- Learning objectives — handle the reality of legal data at scale.
- Topics
- Handling 1,400+ file types, emails, PSTs, scanned paper, load files (.dat/.opt); extracting relevant embedded metadata.
- Text extraction (Tika), OCR, and deduplication strategies.
- Lab: FreeEed Ingestion — build an ingestion pipeline over a deliberately messy document set (emails/PSTs, scans, load files).
Module 3: Search and Retrieval — the Foundation
- Learning objectives — build the core eDiscovery primitive: find anything inside everything.
- Topics — full-text search and indexing (Solr/Lucene); relevance, metadata and date filtering; searching across OCR-d content.
- Lab: eDiscovery Search — index a corpus and run 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 its sources.
- Topics
- Why retrieval, not fine-tuning, for sensitive material — the model never consumes the documents directly.
- Chunking, embeddings, and above all 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 certify its status.
- Topics
- Where data goes when it hits a cloud AI.
- Privilege waiver, duty of competence, and the "private" spectrum (contractual vs. physical).
- Morgan v. V2X case law and why local models are 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 left the premises.
Module 6: Defensible AI Review
- Learning objectives — measure and document an AI review to ensure it holds up in court.
- Topics
- The metrics that matter in court: recall, elusion, precision, ground-truth validation; TAR/active learning.
- Transparency (why was this document coded this way?) and reproducibility — pin the model version, fix settings, log everything.
- 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 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 integration.
- Private/on-prem deployment essentials (containerization; keeping data in-house).
- AI governance for legal in brief, 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 with citations using a local model, measure a defensible review, and package it for private deployment.
- Participants leave with a portfolio project that mirrors the actual job of a legal engineer.
Optional Day 3 / Advanced Modules (delivered as a 3rd day or 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) — richer 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-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.
Requirements
- Proficiency in Python and basic APIs.
- Helpful: User-level familiarity with LLMs (no ML background required—we construct the mental model).
- No legal background required—necessary legal concepts are taught in context.
Audience
- Software/AI engineers transitioning into legal tech.
- Legal-tech company engineers needing deeper domain-specific knowledge.
- Tech-savvy legal, eDiscovery, or information-governance professionals who prefer building over buying.
- Anyone 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
