Course Objective & Scope
Fabricated legal precedents, invented market statistics, and phantom citations have caused costly reputational and regulatory failures for organizations worldwide. This focused program examines the root causes of LLM hallucinations and teaches practical methods to prevent and catch ungrounded claims.
Participants learn the distinction between intrinsic and extrinsic hallucinations, how to constrain models strictly to provided reference documents (source grounding), and how to audit citations against authoritative primary sources. Teams leave with an enforceable Source Grounding and Citation Control Standard.
Who Should Attend?
- Legal, compliance, regulatory affairs, finance, corporate communications, and strategy teams
- Specialists preparing external client deliverables, public statements, tenders, or policy briefs
- Teams working with internal knowledge bases and document Q&A tools
- Basic familiarity with LLM chat tools is sufficient
Key Takeaways
- Distinguish between intrinsic hallucinations (distorting the source) and extrinsic hallucinations (inventing outside facts)
- Enforce strict source grounding so models answer exclusively from supplied documents
- Identify and verify phantom URLs, fabricated regulatory clauses, and misquoted citations
- Design abstention prompts that instruct the model to state when information is missing rather than guessing
- Apply a mandatory primary-source citation protocol for external and high-stakes corporate documents
Curriculum
Anatomy of a Hallucination: Why Models Fabricate
- Probabilistic next-token prediction vs. database lookup: why models struggle to say 'I don't know'
- Intrinsic vs. extrinsic hallucinations: distorting provided text vs. inventing unsupported facts
- High-risk triggers: leading prompts, obscure facts, conflicting context, and long conversation drift
Source Grounding and Abstention Prompting
- Closed-book prompting: restricting the model strictly to uploaded reference material
- Requiring verbatim quotes and section/page identifiers before generating any synthesis
- Engineering abstention rules so the model flags missing information instead of speculating
Auditing Citations, Links, and Regulatory References
- Why LLMs invent plausible URLs, academic DOIs, legal statutes, and case numbers
- Detecting citation misattribution—where a real source is cited for a claim it never made
- Using inline citation cards in NotebookLM, Perplexity, and enterprise RAG tools effectively
Corporate Source Verification Standard Workshop
- High-risk query categories across legal, finance, HR, and public relations
- Red-team exercise: finding subtle factual and citation hallucinations planted in business memos
- Workshop: Drafting a departmental Source Control and Citation Verification Standard
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