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Google: OKF v0.2 introduces Trust Layer including Author, Freshness and more

  • vor 4 Tagen
  • 2 Min. Lesezeit

Aktualisiert: vor 2 Tagen

Key Takeaways:

Google: OKF v0.2 introduces Trust Layer including Author, Freshness and more
  • Google introduced the first update to the recently introduced Open Knowledge Format (OKF) that adds a Trust Layer called OKF v0.2:

    • new sources field: author, usage_count, last_modified

    • generated: { by, at }: how the current content was produced, and when it last meaningfully changed.

    • verified: [ { by, at } ]: a list of independent confirmations against the sources or the underlying resource; a human sign-off, a nightly finance process, or both.

    • status moves a concept through draft → stable → deprecated

    • stale_after is a single absolute date.

    • OKF v0.2 introduces a new concept type, Attested Computation. It carries not just what a value means but a sanctioned way to compute it

  • The aim is to "provide accountability, a consumer (often another agent) has to judge each concept on explicit signals instead, and needs to answer five questions:

    1. What was this created from? (provenance)

    2. How much should I trust it? (trust)

    3. Is it still true? (freshness)

    4. Is it the current version? (lifecycle)

    5. Was this number produced the way we said it must be? (attestation)"

  • This underlines the importance of EEAT (Expertise, Experience, Authority, Trust) not only for SEO but for GEO and AI Search as well - so check your pages and the information you provide on any channel:

    • add who provided a piece of content / information (author)

    • add a last updated

    • name authoritive sources and refer to them


"The updated OKF v0.2 underlines the importance of EEAT (Expertise, Experience, Authority, Trust) not only for SEO but for GEO and AI Search as well - basically for every channel" - David Epding

Example:

---
type: Attested Computation
title: Revenue for a fiscal year
runtime: bigquery
parameters:
 - { name: year, type: integer, required: true }
executor:
  resource: skills/run-on-bq.md
  receipt: [job_id, executed_sql, result]
attester:
  resource: attesters/sql_equality.py
generated: { by: reference_agent/gemini-2.5-pro, at: 2026-06-30T14:00:00Z }
verified:
  - { by: human:jsmith@acme, at: 2026-07-01T09:00:00Z }
status: stable
stale_after: 2026-12-31
sources:
  - id: revenue-policy
    resource: policies/revenue-recognition.md
    title: Revenue Recognition Policy (FY2026)
    author: human:jsmith@acme
    last_modified: 2026-06-15
---
# Computation
SELECT
 SUM(
    CASE
      WHEN o.currency = 'USD' THEN o.net_amount
      ELSE o.net_amount * fx.rate_to_usd
    END
  ) AS revenue_usd
FROM `acme.sales.orders` AS o
LEFT JOIN `acme.finance.fx_daily_rates` AS fx
  ON fx.currency = o.currency
  AND fx.rate_date = DATE(o.order_ts)
WHERE o.order_status = 'delivered'
  AND DATE_DIFF(CURRENT_DATE(), DATE(o.order_ts), DAY) >= 30
  AND EXTRACT(YEAR FROM o.order_ts) = @year


Sources:

© 2026 David Epding.            Erstellt mit Wix.com.

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David Epding ist AI Search (GEO & SEO), Data Analytics und Automation Manager mit über 10 Jahren Erfahrung in Technischem SEO mit breiter Expertise für LLMs und langjähriger Erfahrung in der Daten-Analyse.

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