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Course Outline
Day 1: Building the Foundation — Ingestion, Search, Retrieval
Module 1: The Legal Engineer’s Landscape
- Learning objectives — comprehend the role, AI’s integration in legal work, and two critical risks pervasive in the field.
- Topics
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- The legal engineer role and current market demand.
- AI applications: eDiscovery, review, contracts, research, investigations; EDRM model explained simply.
- Build vs. buy decisions.
- The two omnipresent risks: confidentiality/privilege and defensibility.
Module 2: Legal Data Complexity — Ingestion and Extraction
- Learning objectives — manage the reality of legal data at scale.
- Topics
- Handling 1,400+ file types, email/PST files, scanned paper, load files (.dat/.opt); understanding critical embedded metadata.
- Text extraction (Tika), OCR, and de-duplication strategies.
- Lab: FreeEed Ingestion — build an ingestion pipeline over a deliberately messy document set (email/PST, scans, load files).
Module 3: Search and Retrieval — The Core
- Learning objectives — construct the fundamental 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 perform real eDiscovery-style searches, including within OCR’d scans.
Module 4: RAG for Legal Documents — With Citations
- Learning objectives — implement RAG over legal documents that cites its sources.
- Topics
- Why retrieval, not fine-tuning, is preferred for sensitive material—the model never consumes 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 questions using 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 able to certify its status.
- Topics
- Data flow when using cloud AI services.
- Privilege waiver, duty of competence, and the "private" spectrum (contractual vs. physical).
- Morgan v. V2X case study 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 prove via monitoring that no data exited the system.
Module 6: Defensible AI Review
- Learning objectives — measure and document an AI review to ensure legal validity.
- Topics
- Court-admissible metrics: recall, elusion, precision, ground-truth validation; TAR / active learning.
- Transparency (rationale for coding decisions) and reproducibility — pinning the model, fixing settings, logging all actions.
- The "defensible case snapshot" allowing others to re-run your review 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 — assemble components into a workflow, deploy privately, and evaluate performance.
- Topics
- A multi-step legal workflow (ingest → search → summarize → review → produce) with human-in-the-loop.
- Private/on-premises deployment essentials (containerization; keeping data in-house).
- AI governance for legal use cases 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)
- Construct a private, defensible legal-AI application end-to-end — ingest a messy corpus, search it, answer questions with citations using a local model, measure defensibility, and package for private deployment.
- Participants leave with a portfolio project mirroring the legal engineer’s role.
Optional Day 3 / Advanced Modules (available 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 dive) — advanced 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-premises 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 API usage.
- Helpful: Familiarity with Large Language Models (LLMs) at a user level (no ML background required—we build the conceptual framework).
- No legal background required—essential legal concepts are taught within context.
Audience
- Software and AI engineers transitioning into the legal tech sector.
- Engineers at legal-tech firms requiring deeper domain-specific knowledge.
- Technically oriented 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
