Enterprise Generative Search — Azure and Open Source  ·  View 21 of 41  ·  Data

Freshness Lifecycle

The loop that keeps the index honest about time, and publishes it when it is not.

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Change detected delta, CDC or crawl Prioritised by freshness class Reprocessed only what changed Republished idempotent upsert Probed canary query per source Superseded old version demoted Staleness published as-of on every answer Freshness Loop per source class queued 5 min or 24 h parse and embed alias swap found or alarmed graph edge written back to watching Freshness Lifecycle — Keeping the Index Honest About Time Application we own Decision point Data store Security / platform Staleness is never hidden. Where a source is behind its class, the answer says so rather than reading as current. v 1.0 · owner Data and AI Global Practice

Decisions

  • Staleness is published, never hidden. Where a source is behind its freshness class, answers drawing on it carry an as-of and the confidence is reduced.
  • Supersession is written as a graph edge at publish time, so a retired document can still be retrieved and will be labelled as superseded rather than quietly outranked.
  • A canary query per source proves the loop actually closed. A pipeline that reports success while returning nothing is the failure this catches.

Numbers

  • Freshness classes: collaboration 5 minutes, support 15 minutes, archive and external 24 hours, governed tables query-time.
  • Canary queries run every 10 minutes per source; a miss alerts before a user reports it.

Assumptions

  • Sources provide a usable change feed. Where they do not — the archive — the blob change feed substitutes, at the cost of a coarser freshness class.
  • Stale content is more often useful than harmful, so the platform labels it rather than withholding it. That is a stated risk acceptance, recorded in view 41.
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Solution Architecture v1.0 · Data & AI Global Practice · 2026-09 · 41 views · Microsoft Azure and open source21-freshness-lifecycle