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Course Outline
Day 1: Foundation Building — Ingestion, Search, and Retrieval
Module 1: The Legal Engineer's Perspective
- Learning objectives — Grasp the role, the integration of AI in legal tasks, and the two overarching risks: confidentiality and defensibility.
- Topics
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- The role of the legal engineer and why demand is surging
- AI integration points: eDiscovery, review, contracts, research, and investigations; a plain-language explanation of the EDRM model
- Build vs. Buy strategies
- The two critical risks: confidentiality/privilege and defensibility
Module 2: Managing Messy Legal Data — Ingestion and Extraction
- Learning objectives — Address the practical challenges of processing legal data at scale.
- Topics
- Handling 1,400+ file types, email and PST files, scanned documents, and load files (.dat/.opt); focusing on relevant embedded metadata
- Text extraction (Tika), OCR, and de-duplication strategies
- Lab: FreeEed Ingestion — Construct an ingestion pipeline for a deliberately complex document set (email/PST, scans, load files)
Module 3: Search and Retrieval — The Core Foundation
- Learning objectives — Develop the essential eDiscovery capability: locating any item within a massive corpus.
- Topics — Full-text search and indexing (Solr/Lucene); relevance, metadata and date filtering; searching within OCR-processed content
- Lab: eDiscovery Search — Index a corpus and execute eDiscovery-style searches, including those involving OCR-processed scans
Module 4: RAG for Legal Documents — With Citations
- Learning objectives — Implement RAG over legal documents that includes source citations.
- Topics
- Why retrieval, not fine-tuning, is preferable for sensitive material — ensuring the model does not internalize the documents
- Chunking, embeddings, and most importantly, 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 — Maintain legal data locally and ensure it is certifiable.
- Topics
- Tracking data flow when interacting with cloud AI
- Privilege waiver, duty of competence, and the spectrum of "privacy" (contractual vs. physical)
- Morgan v. V2X and why local solutions are court-defensible
- Serving local models (Ollama / vLLM) and monitoring outbound traffic
- Lab: Local Model + Egress Proof — Run a local model end-to-end and use monitoring to prove that no data left the environment
Module 6: Defensible AI Review
- Learning objectives — Measure and document AI reviews to ensure legal standing.
- Topics
- Court-ready metrics: recall, elusion, precision, ground-truth validation; TAR / active learning
- Transparency (explaining coding decisions) and reproducibility — pinning models, fixing settings, and logging all actions
- Creating a "defensible case snapshot" that allows a review to be re-run later with identical results
- Lab: Defensible Review — Validate an AI review against a blind ground truth and generate a reproducibility bundle
Module 7: Deployment — Workflow, Private Infrastructure, and Governance
- Learning objectives — Integrate components into a workflow, deploy privately, and evaluate the system.
- Topics
- Multi-step legal workflow (ingest → search → summarize → review → produce) with human-in-the-loop oversight
- Key elements 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 — Assemble a multi-step workflow, score it with SAIS-100, and package it for private deployment
Capstone (Integrated across Day 2)
- Develop 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 conclude with a portfolio project that mirrors the actual job of a legal engineer.
Optional Day 3 / Advanced Modules (Available as a 3rd 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: Create a timeline and entity/relationship view.
- Agentic and Multi-Step Legal Workflows (Advanced) — Enhanced 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-prem 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 multiple workers.
- Governance and Compliance Deep-Dive — The AI regulatory landscape (100+ US state AI laws, the EU AI Act), audit requirements, and a complete SAIS-100 governance audit. Lab: Audit a legal-AI system against a governance/defensibility checklist.
Requirements
- Proficiency in Python and basic API usage.
- Beneficial: Familiarity with LLMs at a user level (no machine learning background is required—we will build the necessary mental models).
- No legal background is necessary—essential legal concepts are taught within the context of the technical work.
Audience
- Software and AI engineers transitioning into legal tech.
- Engineers at legal-tech companies requiring deeper domain expertise.
- Technically proficient legal, eDiscovery, and information-governance professionals who prefer building solutions over purchasing them.
- Professionals aiming for “legal engineer” or “AI legal engineer” roles.
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
