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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 critical risks that permeate everything.
- Topics
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- The legal-engineer role and current market demand.
- AI applications: eDiscovery, review, contracts, research, investigations; the EDRM model explained simply.
- Build vs. buy decisions.
- The two pervasive risks: confidentiality/privilege and defensibility.
Module 2: Legal Data Is Messy — Ingestion and Extraction
- Learning objectives—handle the reality of managing legal data at scale.
- Topics
- 1,400+ file types, emails, PST files, scanned paper, load files (.dat/.opt); 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—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 inside 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, is preferred for sensitive material—the model never consumes the documents directly.
- Chunking, embeddings, and critically, 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
- Data movement when interacting with cloud AI services.
- Privilege waiver, duty of competence, and the “private” spectrum (contractual vs. physical).
- Morgan v. V2X case study and why local hosting 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 egressed.
Module 6: Defensible AI Review
- Learning objectives—measure and document an AI review so it holds up in court.
- Topics
- Court-admissible metrics: recall, elusion, precision, ground-truth validation; TAR / active learning.
- Transparency (why did it code this document?) and reproducibility—pin the model, fix settings, log everything.
- The “defensible case snapshot” allowing someone to re-run your review a year 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 the pieces into a workflow, deploy it privately, and score it.
- Topics
- A multi-step legal workflow (ingest → search → summarize → review → produce) with human-in-the-loop.
- Private/on-prem deployment essentials (containerize; keep data in the building).
- AI governance for legal, and scoring the system with 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 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/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)—richer orchestration, contract analysis, multi-doc synthesis, tool use, and guardrails as a design principle. Lab: build a multi-step workflow with a human checkpoint.
- Deployment at Scale—on-prem 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: Familiarity with Large Language Models (LLMs) at a user level. No ML background is required as we build the mental model from scratch.
- No legal background required—the necessary legal concepts are taught within context.
Audience
- Software and AI engineers transitioning into legal technology.
- Engineers at legal-tech companies requiring deeper domain knowledge in law.
- Technically-oriented legal, eDiscovery, or information-governance professionals who wish to build solutions rather than just purchase them.
- Individuals targeting 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
