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Course Outline
Day 1: Build the Foundation — Ingest, Search, Retriev
Module 1: The Legal Engineer’s Landscape
- Learning objectives — understand the role, where AI fits in legal work, and the two risks that run through everything.
- Topics
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- The legal-engineer role and why it is being hired right now
- Where AI fits: eDiscovery, review, contracts, research, investigations; the EDRM model in plain terms
- Build vs. buy
- The two risks that run through everything: confidentiality/privilege and defensibility
Module 2: Legal Data Is Messy — Ingestion and Extraction
- Learning objectives — handle the reality of legal data at scale.
- Topics
- 1,400+ file types, email and PST, scanned paper, load files (.dat/.opt); embedded metadata that matters
- Text extraction (Tika), OCR, and de-duplication as a choice
- Lab: FreeEed Ingestion — build 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: find anything inside everything.
- Topics — full-text search and indexing (Solr/Lucene); relevance, metadata and date filtering; search across OCR’d content
- Lab: eDiscovery Search — index a corpus and run real eDiscovery-style searches, including inside 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, for sensitive material — the model never swallows the documents
- Chunking, embeddings, and above all citations / provenance
- Multi-document and thread summarization
- Lab: Legal RAG with Citations — build a RAG Q&A over a document set that answers 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 be able to certify it.
- Topics
- Where the data actually goes when it hits a cloud AI
- Privilege waiver, duty of competence, and the “private” spectrum (contractual vs. physical)
- Morgan v. V2X and why local 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, with monitoring, that no data egressed
Module 6: Defensible AI Review
- Learning objectives — measure and document an AI review so it holds up.
- Topics
- The numbers that hold up in court: recall, elusion, precision, ground-truth validation; TAR / active learning
- Transparency (why did it code this document?) and reproducibility — pin the model, fix the settings, log everything
- The “defensible case snapshot” that lets someone re-run your review a year later and get the same result
- 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 the pieces into a workflow, deploy it privately, and score it.
- Topics
- A multi-step legal workflow (ingest → search → summarize → review → produce) with human-in-the-loop
- Private/on-prem deployment essentials (containerize; keep the data in the building)
- AI governance for legal in brief, and scoring the system with SAIS-100 (the Elephant Scale Secure AI Score)
- Lab: Score and Package — wire 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 end to end — ingest a messy corpus, search it, answer questions over it with citations using a local model, measure a defensible review, and package it for private deployment.
- Participants leave with a portfolio project that is the legal-engineer job.
Optional Day 3 / Advanced Modules (deliverable as a 3rd day or a 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 a design principle. 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, the EU AI Act), audit requirements, and a full SAIS-100 governance audit. Lab: audit a legal-AI system against a governance/defensibility checklist.
Requirements
- Comfortable with Python and basic APIs
- Helpful: familiarity with LLMs at a user level (no ML background required — we build the mental model)
- No legal background required — the legal concepts you need are taught in context
Audience
- Software / AI engineers moving into legal tech
- Legal-tech company engineers who need legal-domain depth
- Technically-minded legal / eDiscovery / information-governance professionals who want to build, not just buy
- Anyone targeting the “legal engineer” / “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
