GPT-6 Astra Internal Knowledge Base: Stop Shipping Unsourced Answers

A proposed GPT-6 Astra internal knowledge-base workflow emphasizes citation-first decision support rather than uploading entire document collections into a model, arguing that reliable answers require approved evidence packets containing source IDs, document status, owners, effective dates, relevant excerpts, and explicit conflicts. It recommends keeping permissions, source governance, retention controls, audit logs, and final authority in controlled document systems and with named human owners, while using a lower-cost model for document inventory, classification, duplicate detection, sensitivity flags, and evidence manifests before Astra handles high-value conflict resolution and customer-safe synthesis. The process includes testing whether superseded or draft documents improperly support claims, requiring every answer to state citations, uncertainty, and escalation owners, and treating embedded instructions in source material as untrusted. Three example applications cover regional workload migration, security questionnaires about retention and deletion, and sales beta-release guidance, with heightened human review for contractual, privacy, security, retention, and other high-risk commitments. The text also advises routing premium model usage toward difficult adjudication rather than broad corpus scanning, measuring costs through token usage and human-review time, and accepting “not established by supplied sources” as a valid outcome when evidence is insufficient.

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