Implementation guide
Keep AI Outputs Grounded in Reliable Sources
Detailed training workflow for Keep AI Outputs Grounded in Reliable Sources in Playbooks: Core Systems.
playbookvaultknowledgegovernancetutorial
Guided walkthrough
The Problem: teams upload random files, nobody curates them, and prompts start returning inconsistent answers. Source Inventory List source documents by type: policy, product docs, legal templates, customer playbooks. Ownership Assign one owner per vault folder and set review cadence. Version Labels Tag each document with version, effective date, and status (draft/published/archived). Prompt Injection Rules Allow only published documents in production prompt context. Document Metadata Standard required_metadata: -
doc_owner - effective_date - review_cycle_days - status - sensitivity_level - source_system - linked_use_cases
Advanced implementation notes
Knowledge Reliability Framework Content Lifecycle Enforce Draft -> Review -> Published -> Archived states with approval evidence. Staleness Detection Flag documents that exceed review SLA and block them from high-risk prompts. Source Weighting Prefer canonical documents (policy master, legal approved template) over ad-hoc notes. Conflict Resolution Detect contradictory clauses across files and escalate to document owner. Traceability Attach source references to every generated output for audit and debugging. Run monthly vault hygiene reports: stale docs,
orphan docs, and duplicate templates. Restrict write access for high-risk folders to trained maintainers. Use tags that map directly to production use cases. Do not inject draft content into customer-facing responses. Do not keep multiple conflicting 'final' files in the same folder. Do not skip archive state. Deletion destroys auditability.