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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, the integration of AI in legal work, and the two critical risks that permeate all activities.
- Topics
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- The legal-engineer role and its current market demand.
- AI integration points: eDiscovery, review, contracts, research, investigations; EDRM model explained simply.
- Build vs. buy considerations.
- The two universal risks: confidentiality/privilege and defensibility.
Module 2: Legal Data Is Messy—Ingestion and Extraction
- Learning objectives—handle the reality of large-scale legal data processing.
- Topics
- Processing 1,400+ file types, emails, PSTs, scanned papers, load files (.dat/.opt); critical embedded metadata.
- Text extraction (Tika), OCR, and deduplication strategies.
- Lab: FreeEed Ingestion—construct 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 within 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 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 its sources.
- Topics
- Why retrieval, not fine-tuning, is preferred for sensitive material—the model never ingests 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 queries 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 provide certification of that status.
- Topics
- Data movement when interacting with cloud AI services.
- Privilege waiver, duty of competence, and the spectrum of "privacy" (contractual vs. physical).
- The precedent of Morgan v. V2X 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 exited the environment.
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 was this document coded?) and reproducibility—pin the model version, fix settings, log everything.
- The "defensible case snapshot" enabling re-runs 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 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 in-house).
- 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 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 for private deployment.
- Participants leave with a portfolio project representing the core work of a legal engineer.
Optional Day 3 / Advanced Modules (deliverable 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)—richer orchestration, contract analysis, multi-doc synthesis, tool use, and guardrails as design principles. 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, 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 but not required: Familiarity with Large Language Models (LLMs) at a user level (no ML background needed—we build the conceptual framework).
- No legal background required—essential legal concepts are taught within context.
Target Audience
- Software and AI engineers transitioning into legal technology.
- Engineers at legal-tech companies needing deeper domain expertise.
- Technically-oriented legal, eDiscovery, or information governance professionals who wish to build solutions rather than just procure them.
- Professionals 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
