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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 integration of AI in legal work, and the two critical risks prevalent across all projects.
- Topics
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- The role of the legal engineer and the current demand for this skill set.
- Where AI fits: eDiscovery, review, contracts, research, and investigations; understanding the EDRM model in simple terms.
- Deciding between building versus buying solutions.
- The two overarching risks: confidentiality/privilege and defensibility.
Module 2: Handling Messy Legal Data—Ingestion and Extraction
- Learning Objectives—manage the realities 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 as strategic choices.
- Lab: FreeEed Ingestion—build an ingestion pipeline using a deliberately messy document set (email/PST, scans, load files).
Module 3: Search and Retrieval—The Core Foundation
- Learning Objectives—establish the core eDiscovery capability: finding any information within any dataset.
- Topics—full-text search and indexing (Solr/Lucene); relevance, metadata, and date filtering; searching across OCR-processed content.
- Lab: eDiscovery Search—index a corpus and execute real eDiscovery-style searches, including within OCR-processed scans.
Module 4: RAG for Legal Documents—With Citations
- Learning Objectives—build RAG for legal documents that accurately cites sources.
- Topics
- Why retrieval, rather than fine-tuning, is preferred for sensitive material—ensuring the model does not internally retain document content.
- Chunking, embeddings, and the critical importance of 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: Ensuring Privacy, Defensibility, and Deployability
Module 5: Privacy, Privilege, and Local Serving—The Privilege Trap
- Learning Objectives—keep legal data local and ensure its status is certifiable.
- Topics
- Understanding where data goes when processed by cloud AI.
- Privilege waiver, duty of competence, and the spectrum of “privacy” (contractual versus physical).
- The Morgan v. V2X case 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 demonstrate, via monitoring, that no data has egressed.
Module 6: Defensible AI Review
- Learning Objectives—measure and document AI reviews to ensure they are legally robust.
- Topics
- Metrics that hold up in court: recall, elusion, precision, ground-truth validation; TAR / active learning.
- Transparency (explaining document coding decisions) and reproducibility—pinning models, fixing settings, and logging all actions.
- Creating a “defensible case snapshot” that allows someone to re-run the review a year later with identical results.
- Lab: Defensible Review—measure an AI review against a blind ground truth and generate a reproducibility bundle.
Module 7: Deployment—Workflow, Private Hosting, and Governance
- Learning Objectives—integrate components into a workflow, deploy privately, and evaluate performance.
- Topics
- Designing multi-step legal workflows (ingest → search → summarize → review → produce) with human-in-the-loop controls.
- Essentials for private/on-premises deployment (containerization; keeping data within the organization).
- Brief overview of legal AI governance and system scoring using SAIS-100 (the Elephant Scale Secure AI Score).
- Lab: Score and Package—assemble 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 from 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 will leave with a portfolio project that exemplifies the legal engineer role.
Optional Day 3 / Advanced Modules (Available as a Third Day or Modular Series)
- Investigations: Entities, Relationships, and Timelines—extract people/organizations/dates, reconstruct email threads, build chronologies, and map near-duplicates and document lineage. Lab: Construct a timeline and entity/relationship view.
- Agentic and Multi-Step Legal Workflows (Advanced)—advanced orchestration, contract analysis, multi-document synthesis, and tool use with 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 high volumes, regulated environments (CJIS, government, higher education), and 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 comprehensive SAIS-100 governance audit. Lab: Audit a legal-AI system against a governance/defensibility checklist.
Requirements
- Proficiency in Python and basic APIs.
- Beneficial: Familiarity with LLMs at a user level (no machine learning background required—we will build the necessary mental model).
- No legal background required—essential legal concepts are taught within context.
Audience
- Software and AI engineers transitioning into legal tech.
- Engineers at legal-tech companies seeking deeper domain expertise.
- Technically inclined legal, eDiscovery, or information governance professionals who prefer building solutions over purchasing them.
- 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
