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Course Outline
Day 1: Building the Foundation — Ingesting, Searching, Retrieving
Module 1: The Legal Engineer’s Landscape
- Learning objectives — Understand the role, AI integration in legal work, and two critical risks inherent to the field.
- Topics
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- The emerging legal-engineer role and market demand.
- AI applications: eDiscovery, review, contracts, research, investigations; simplified EDRM model overview.
- Build versus buy decisions.
- Cross-cutting risks: confidentiality/privilege and defensibility.
Module 2: Handling Messy Legal Data — Ingestion and Extraction
- Learning objectives — Manage the complexities of legal data at scale.
- Topics
- Over 1,400 file types, email/PST archives, scanned documents, load files (.dat/.opt); critical embedded metadata.
- Text extraction (Tika), OCR, and deduplication strategies.
- Lab: FreeEed Ingestion — Construct an ingestion pipeline processing a deliberately complex dataset (emails/PSTs, scans, load files).
Module 3: Search and Retrieval — The Core Primitive
- Learning objectives — Develop the foundational eDiscovery capability: finding specific information within vast datasets.
- Topics — Full-text search and indexing (Solr/Lucene); relevance scoring, metadata, and date filtering; searching OCR-processed content.
- Lab: eDiscovery Search — Index a corpus and execute real-world eDiscovery queries, including within OCR-processed scans.
Module 4: RAG for Legal Documents — With Citations
- Learning objectives — Implement RAG over legal documents with source citation capabilities.
- Topics
- Retrieval over fine-tuning for sensitive data — preventing the model from ingesting proprietary documents.
- Chunking, embeddings, and critically,citations / provenance.
- Summarizing multiple documents and conversation threads.
- Lab: Legal RAG with Citations — Construct a RAG-based Q&A system that answers queries with sourced citations.
Day 2: Ensuring Privacy, Defensibility, and Deployment Readiness
Module 5: Privacy, Privilege, and Local Serving — Avoiding the Privilege Trap
- Learning objectives — Keep legal data local and ensure certification of privacy.
- Topics
- Data flow analysis when using cloud AI services.
- Privilege waiver risks, competence duties, and the spectrum of "privacy" (contractual vs. physical).
- Morgan v. V2X case study: why local deployment is court-defensible.
- Serving local models (Ollama / vLLM) and monitoring outbound network traffic.
- Lab: Local Model + Egress Proof — Run a local model end-to-end and verify via monitoring that no data left the premises.
Module 6: Defensible AI Review
- Learning objectives — Measure and document an AI review process to ensure validity.
- Topics
- Courts require specific metrics: recall, elusion, precision, ground-truth validation; TAR / active learning.
- Transparency (rationale for coding decisions) and reproducibility — locking model versions, fixing settings, logging all actions.
- Creating a "defensible case snapshot" to allow re-running the review with identical results later.
- Lab: Defensible Review — Evaluate an AI review against blind ground truth and produce a reproducibility package.
Module 7: Deployment — Workflows, Private Infrastructure, and Governance
- Learning objectives — Integrate components into a workflow, deploy privately, and evaluate performance.
- Topics
- Multi-step legal workflows (ingest → search → summarize → review → produce) with human-in-the-loop processes.
- Private/on-premises deployment essentials (containerization; data residency).
- Overview of legal AI governance and system scoring using SAIS-100 (The Elephant Scale Secure AI Score).
- Lab: Score and Package — Wire a multi-step workflow, apply SAIS-100 scoring, and package for private deployment.
Capstone Project (Integrated into Day 2)
- Construct a private, defensible legal-AI application from start to finish — ingest unstructured data, search it, answer queries with citations using a local model, evaluate review defensibility, and package for private deployment.
- Participants depart with a portfolio-ready project that embodies the legal engineer role.
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, map near-duplicates and document lineage. Lab: Construct a timeline and entity/relationship view.
- Agentic and Multi-Step Legal Workflows (Advanced) — Enhanced orchestration, contract analysis, multi-document synthesis, tool use, and guardrail design. Lab: Build a multi-step workflow with human checkpoint integration.
- Deployment at Scale — On-premises and appliance deployment, distributed processing for high volumes, regulated environments (CJIS, government, higher education), hardware sizing. Lab: Containerize and scale a processing job across multiple workers.
- Governance and Compliance Deep-Dive — AI regulatory landscape (100+ US state laws, EU AI Act), audit requirements, full SAIS-100 governance audit. Lab: Audit a legal-AI system against a governance/defensibility checklist.
Requirements
- Familiarity with Python and basic APIs
- Beneficial: User-level understanding of LLMs (no machine learning background required — we build the conceptual framework)
- No prior legal background required — necessary legal concepts are taught within context
Target Audience
- Software and AI engineers transitioning into legal technology.
- Engineers at legal-tech companies requiring deeper domain knowledge.
- Technically inclined legal professionals, eDiscovery specialists, and information governance experts who wish to build solutions rather than merely purchase them.
- Individuals 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
