Model Online / On-Site
Duration
Modules 4 modules

Course Objective & Scope

When internal AI assistants or enterprise RAG systems give wrong answers, the culprit is rarely the LLM—it is outdated policies, duplicate slide decks, unsearchable PDFs, and conflicting wiki pages in the corporate repository. This course teaches business and knowledge teams how to make institutional knowledge AI-ready.

Participants learn to audit departmental repositories, establish a Single Source of Truth (SSOT), apply metadata schemas, and structure SOPs and FAQs so chunking and retrieval pipelines preserve full context. Teams leave with a practical Knowledge Governance & Context Hygiene Standard.

Who Should Attend?

  • Knowledge managers, quality assurance specialists, process owners, and documentation leads
  • Business unit leads building workspace assistants (Claude Projects, Copilot agents, enterprise RAG)
  • HR, customer support, IT service desk, legal, and product enablement teams managing internal wikis
  • No programming required

Key Takeaways

  • Audit corporate repositories to eliminate duplicate, obsolete, and conflicting documents that poison AI responses
  • Author AI-ready documents with self-contained sections, explicit headings, and clear acronym glossaries
  • Design metadata schemas (effective date, owner, audience, confidentiality tier) to anchor context retrieval
  • Reformat complex tables, flowcharts, and scanned PDFs into machine-readable structures
  • Establish a continuous knowledge maintenance lifecycle and ownership model for departmental AI context

Curriculum

Why Corporate Repositories Fail in AI Systems
  • How duplicate drafts, legacy policies, and undocumented tribal knowledge cause contradictory AI answers
  • Human-oriented formatting vs. LLM-friendly structure: why visual layout often breaks text extraction
  • The chunking problem: how cross-references like 'see table above' lose meaning when split for retrieval
AI-Ready Document and Wiki Authoring Standards
  • Writing self-contained, modular document sections that retain context even when retrieved in isolation
  • Converting scanned PDFs, image-based flowcharts, and merged-cell tables into clean Markdown and text
  • Building an explicit Enterprise Glossary to resolve internal acronyms, product codenames, and jargon
Metadata Schemas, Versioning, and Access Boundaries
  • Attaching explicit document headers: Effective Date, Status (Active/Deprecated), Owner, and Scope
  • Mapping knowledge assets to role-based access levels so assistants never leak restricted policies
  • Curating canonical FAQ pairs and golden reference examples to anchor assistant behavior
Knowledge Governance and Repository Audit Workshop
  • Using LLMs to audit existing policy folders for contradictions, outdated dates, and coverage gaps
  • Establishing a knowledge lifecycle: review cadences, deprecation workflows, and content ownership
  • Workshop: Auditing a real departmental document set and designing an AI-Ready Knowledge Standard

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