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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, how AI fits into legal work, and the two critical risks that permeate all activities.
- Topics
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- The legal-engineer role and current market demand
- AI applications: eDiscovery, review, contracts, research, investigations; explaining the EDRM model clearly
- Build versus 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 legal data at scale.
- Topics
- 1,400+ file types, email/PST formats, scanned paper, load files (.dat/.opt); critical embedded metadata
- Text extraction (Tika), OCR, and de-duplication 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: finding 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 perform 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, is preferred for sensitive material—the model never consumes the raw documents
- 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 be able to certify it.
- Topics
- Where data actually goes 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 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 left the premises
Module 6: Defensible AI Review
- Learning objectives — measure and document an AI review so it holds up in court.
- Topics
- The numbers that hold up in court: 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" that allows someone to re-run your review a year later and get 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 components 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 processes
- Private/on-premises deployment essentials (containerization; keeping data within the building)
- AI governance for legal briefly, 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)
- Construct a private, defensible legal-AI application end-to-end — ingest a messy corpus, search it, answer questions over it 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 work of a legal engineer.
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 dive) — 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-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: Familiarity with Large Language Models (LLMs) at a user level (no ML background required—we develop the necessary mental models).
- No legal background required—essential legal concepts are taught within context.
Audience
- Software or AI engineers transitioning into legal technology.
- Engineers at legal-tech companies seeking deeper legal-domain expertise.
- Technically-minded legal, eDiscovery, or information-governance professionals who prefer to build rather than buy.
- 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
