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Duration 14 hours
Course Outline
Day 1: Building the Foundation — Ingestion, Search, and Retrieval
Module 1: The Legal Engineer’s Landscape
- Learning objectives—understand the role, the fit of AI in legal work, and the two pervasive risks.
- Topics
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- The legal-engineer role and the current demand for it.
- Where AI fits: eDiscovery, review, contracts, research, and investigations; an explanation of the EDRM model.
- Deciding between building custom solutions versus buying existing ones.
- The two critical risks: confidentiality/privilege and defensibility.
Module 2: Legal Data is Messy — Ingestion and Extraction
- Learning objectives—handling the reality of large-scale legal data.
- Topics
- Handling 1,400+ file types, email and PST files, scanned paper, and load files (.dat/.opt); the importance of embedded metadata.
- Text extraction (Tika), OCR, and de-duplication strategies.
- Lab: FreeEed Ingestion — Construct an ingestion pipeline for a deliberately messy document set (email/PST, scans, load files).
Module 3: Search and Retrieval — The Foundation
- Learning objectives—building the core eDiscovery primitive: finding anything within everything.
- Topics—full-text search and indexing (Solr/Lucene); relevance, metadata, and date filtering; searching within OCR’d content.
- Lab: eDiscovery Search — Index a corpus and perform realistic eDiscovery-style searches, including within OCR’d scans.
Module 4: RAG for Legal Documents — With Citations
- Learning objectives—building RAG over legal documents that cites its sources.
- Topics
- Why retrieval, not fine-tuning, is preferred for sensitive material—the model does not ingest the documents.
- Chunking, embeddings, and the critical importance of citations/provenance.
- Summarization across multiple documents and threads.
- Lab: Legal RAG with Citations — Build a RAG Q&A system over a document set that answers questions with source citations.
Day 2: Ensuring Privacy, Defensibility, and Deployability
Module 5: Privacy, Privilege, and Local Serving — The Privilege Trap
- Learning objectives—keeping legal data local and ensuring it is certifiable.
- Topics
- Tracing data movement when interacting with cloud AI.
- Privilege waiver, duty of competence, and the spectrum of “privacy” (contractual vs. physical).
- The case of Morgan v. V2X and why local processing 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, through monitoring, that no data has egressed.
Module 6: Defensible AI Review
- Learning objectives—measuring and documenting AI reviews to ensure they hold up under scrutiny.
- Topics
- Critical metrics for court: recall, elusion, precision, ground-truth validation; TAR / active learning.
- Transparency (explaining how documents were coded) and reproducibility—pinning the model, fixing settings, and logging all actions.
- Creating a “defensible case snapshot” that allows a review to be re-run 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: Deployment — Workflow, Private Hosting, and Governance
- Learning objectives—assembling components into a workflow, deploying privately, and scoring the system.
- Topics
- Designing a multi-step legal workflow (ingest → search → summarize → review → produce) with human-in-the-loop controls.
- Essentials for private/on-prem deployment (containerization; keeping data on-site).
- Brief overview of AI governance for legal contexts, and system scoring 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 day-to-day responsibilities of a legal engineer.
Optional Day 3 / Advanced Modules (Available as a third day or a modular series)
- Investigations: Entities, Relationships, and Timelines — Extract people/orgs/dates, reconstruct email threads, build chronologies, and map near-duplicates and document lineage. Lab: Create a timeline and entity/relationship view.
- Agentic and Multi-Step Legal Workflows (Deep Dive) — Advanced orchestration, contract analysis, multi-doc synthesis, and treating tool use and guardrails as core design principles. Lab: Build a multi-step workflow with human checkpoints.
- Deployment at Scale — On-prem and appliance deployment, distributed processing for high volumes, and handling regulated environments (CJIS, government, higher education), including 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 comprehensive SAIS-100 governance audit. Lab: Audit a legal-AI system against a governance/defensibility checklist.
Requirements
- Proficiency with Python and basic API integration.
- Beneficial: familiarity with LLMs at a user level (no machine learning background required—the course builds the necessary mental models).
- No legal background required—essential legal concepts are taught within the context of the technical tasks.
Audience
- Software and AI engineers transitioning into legal tech.
- Engineers at legal-tech companies requiring depth in legal domain knowledge.
- Technically inclined legal, eDiscovery, and information governance professionals who prefer building solutions over purchasing off-the-shelf products.
- Professionals targeting the “legal engineer” or “AI legal engineer” role.
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
