Editorial QA and E-E-A-T for page systems

Transcript of the narration, chapter by chapter.

0:00 Editorial QA and E-E-A-T

Nobody can read twenty thousand pages. So how do you keep quality high at scale? Not with hope, and not with an author box pasted on every page. In this lecture you'll learn how to make E-E-A-T concrete for page systems, the accountability pages every system needs, a scoring rubric, a smart sampling approach, the automated checks that run on every page, and how to disclose AI use honestly.

0:30 Why it matters

Why does this matter? Because trust is the quality signal that takes longest to build and fastest to lose. Here's an analogy. A factory making thousands of products a day doesn't inspect every single one by hand. It uses automated sensors on the line, random samples pulled for detailed inspection, and a named quality manager who signs off. Editorial QA for page systems works the same way. Automated checks on every page, human review on smart samples, and experts who put their names on the work.

1:07 E-E-A-T, concretely

Google's quality rater guidelines describe experience, expertise, authoritativeness and trustworthiness, with trust the most important. Raters don't change rankings directly, but the guidelines describe what Google's systems aim to reward. For page systems, experience means first-hand data: your bookings, testing, inspections, photos and real reviews. Expertise means named experts who write or review guidance. Authority means recognition: citations, links and partnerships. And trust means accurate data, clear methodology, transparent ownership, corrections and honest disclosures.

1:39 Accountability pages

Every page system needs accountability pages. About us, with ownership and contact. Methodology: how data is collected, verified, computed and updated. An editorial policy: who writes and reviews, how AI is used, and how errors get corrected. Author and reviewer profiles. A review policy that follows fake review rules. And advertising or affiliate disclosures if the pages make money. Link them from every template, including a contextual how we compiled this link near the data.

2:12 The rubric (0–2 each)

Next, a rubric. Score sampled pages zero to two on eight criteria. Does it answer the query, answer first? Does it have substantial unique data? Is it accurate? Fresh, with honest dates? Readable and natively localized? Are trust signals visible? If there's AI text, is it specific, grounded and reviewed? And is there a clear, useful next step? Set a publishing bar, calibrated on your own best pages, for example no zeros on accuracy.

2:44 Simple example: rubric scoring (illustrative)

Here's a simple worked example of the rubric in action. An editor scores a page for tutors of chemistry in Rawalpindi. Answers the query: two, the count and rate range are at the top. Unique data: two, verified tutors and booking-based rates. Accuracy: one, one tutor's listed languages are out of date. Freshness: two. Localization: two. Trust signals: one, the methodology link is missing from this template. AI text quality: two. Next step: two. The average passes the bar, but the methodology link issue affects the whole template, so it's fixed once for every page.

3:25 Sampling strategy

Who reviews what? For your-money-or-your-life content like health, finance and legal, review every page before it's indexed. For everything else, use a stratified sample: a few pages from each segment, like city tier, category and data richness, plus every page flagged by automated checks. And increase sampling after any template, data or model change. The lesson has a short Python script that builds this review queue.

3:54 Automated checks

Automated checks run on every page. Data completeness and thresholds. Similarity to the nearest sibling page. Number consistency between the text, tables and structured data. A banned claims list, broken links, missing images or alt text. Language detection, so your Arabic pages really are in Arabic. And readability metrics, but only as a weak signal.

4:18 AI disclosure

What about disclosing AI use? Google suggests considering it where readers would reasonably expect it. A short, honest editorial policy works well, like: review summaries are generated with AI from verified reviews and checked by our editors. It builds trust and fits emerging transparency norms. In the EU, the AI Act has transparency rules for AI-generated text published to inform the public on matters of public interest, unless it's had human review and editorial responsibility. Check how that applies to you.

4:53 Example: London legal directory (illustrative)

Here's an illustrative example. A London legal services directory builds legal service in borough pages. Every guidance section is written by a qualified solicitor, named, with their regulator registration referenced. Directory data is verified against the regulator's register. There's a methodology page, full review before indexing because it's YMYL, and quarterly re-verification. AI is only used to tag practice areas from firm descriptions, with sampling.

5:21 Mistakes + try this now

Common QA mistakes. Pasting the same author box everywhere without real expert involvement. Only reviewing random pages, so problems in one segment go unnoticed. No way for users to report errors. Treating E-E-A-T as a checklist instead of real evidence. And skipping full review for health, finance or legal pages. Try this now: pick ten pages from different segments of your page system and score them with the rubric. Look for a pattern in the lowest scores. It usually points to one template or data fix that lifts hundreds of pages at once.

6:01 Quick self-check

Quick self-check. Your automated checks pass every page, but an editor notices that all pages for one city list tutors who moved away months ago. What does that tell you? Pause. Your automated checks test structure, not truth. The data feed for that city isn't being refreshed. The fix isn't more editing; it's a freshness rule in the pipeline and a correction path for users. That's why human sampling by segment matters: it catches problems machines don't know to look for.

6:36 Watch me do it: first QA cycle (illustrative)

Watch me do it. Let's run a first editorial QA cycle on an illustrative UAE home-maintenance directory. Step one, the queue: the sampling script picks five pages from each of six segments, plus eleven pages flagged by automated checks, forty-one in total. Step two, two editors score them with the rubric independently, then compare. They disagree on seven pages, mostly on freshness. We clarify the rubric: fresh means verified within ninety days for provider details. Step three, patterns: pages in Sharjah score low on accuracy, because provider phone numbers were imported from an old partner file. Pages in Abu Dhabi score low on localization; the Arabic guidance was machine translated without review. Step four, fixes at the source: a re-verification job for the Sharjah providers, and a native Arabic editor for the Abu Dhabi guidance template. Step five, trust signals: we add a visible last-verified date and a report-an-error link to the template. Step six, follow-up: two weeks later, the same segments are re-sampled. Scores improve, and the error reports from users become a new input to the QA queue. Quality at scale is this loop, repeated.

7:57 Recap and next step

Recap. Quality at scale combines automated checks, stratified human sampling, expert ownership and visible accountability. Make E-E-A-T concrete with first-hand data and named experts. Score with a rubric, review YMYL fully, and disclose AI honestly. Your next step: adapt the rubric to your page type, set a calibrated publishing bar, and write your editorial policy paragraph on AI use.

Quality at scale is a process, not a hope

A human cannot read 20,000 pages. A quality system combines automated checks on every page, statistical sampling for human review, expert ownership of page types, and visible accountability signals for users.

E-E-A-T in practice

Google's quality rater guidelines describe Experience, Expertise, Authoritativeness and Trustworthiness (with trust the most important). Raters don't directly change rankings, but the guidelines describe what Google's systems aim to reward. For page systems, E-E-A-T becomes concrete:

Experience: first-hand data (your bookings, your testing, your inspections), photos you took, real user reviews.
Expertise: named experts who write or review guidance for each page type; credentials relevant to the topic.
Authoritativeness: recognition — citations, links, mentions, partnerships.
Trust: accurate data, clear sourcing and methodology, transparent ownership, contact details, correction policies, secure site, honest ads and affiliate disclosures.

Accountability pages every page system needs

About / who we are — the organization, ownership, contact.
Methodology — how data is collected, verified, computed, updated.
Editorial policy — who writes/reviews, how AI is used, how errors are corrected.
Author/reviewer profiles — for guidance content.
Review policy — how reviews are collected and moderated (fake review rules).
Advertising and affiliate disclosure — if pages monetize.

Link these from every template (footer and contextual "How we compiled this" links).

The QA rubric

Score sampled pages 0–2 on each criterion:

Criterion	0	1	2
Answers the query	Not really	Partly	Fully, answer-first
Unique data	None beyond keyword	Some	Substantial, distinct
Accuracy	Errors found	Minor issues	Verified correct
Freshness	Stale	Acceptable	Current and dated honestly
Readability & localization	Poor	OK	Clear, native language
Trust signals	Missing	Partial	Methodology, sources, ownership visible
AI text quality (if any)	Generic/inaccurate	Acceptable	Specific, grounded, reviewed
Conversion/next step	None	Weak	Clear and useful

Set a publishing bar (e.g., average ≥ 1.5 with no zeros on accuracy) — illustrative; calibrate on your own best pages.

Sampling for human review

Review 100% of pages for YMYL or regulated content before indexing.
For other types, review a stratified sample: some pages from each segment (city tier, category, data richness), plus pages flagged by automated checks.
Increase sampling after template, data or model changes.

import random
import pandas as pd

pages = pd.read_csv("pages.csv")  # url, page_type, segment, auto_flags
flagged = pages[pages["auto_flags"] > 0]
sample = (pages[pages["auto_flags"] == 0]
          .groupby(["page_type", "segment"], group_keys=False)
          .apply(lambda g: g.sample(n=min(len(g), 5), random_state=42)))
review_queue = pd.concat([flagged, sample]).drop_duplicates("url")
review_queue.to_csv("review_queue.csv", index=False)
print(len(review_queue), "pages queued for editorial review")

Automated checks on every page

Data completeness and threshold checks.
Similarity to nearest sibling below threshold.
Number consistency between text, tables and structured data.
Banned claims list; profanity; broken links; missing images/alt text.
Language detection (Arabic pages actually in Arabic).
Readability metrics as a weak signal only.

Disclosure of AI use

Google suggests considering disclosure where readers would reasonably expect it. A simple, honest editorial policy ("Review summaries are generated with AI from verified reviews and checked by our editors") builds trust and aligns with emerging transparency norms (such as the EU AI Act's transparency provisions for AI-generated text published to inform the public on matters of public interest, which apply unless the text has undergone human review and editorial responsibility — check how they apply to you).

Worked example: a London legal-services directory

"[legal service] solicitors in [borough]" pages: every guidance section written by a qualified solicitor (named, with SRA registration referenced), directory data verified against the regulator's register, methodology page, 100% review before indexing (YMYL), quarterly re-verification. AI is used only for tagging practice areas from firm descriptions, with sampling.

Pitfalls

"E-E-A-T" as an author box pasted everywhere without real expertise.
Reviewing only random pages (misses systematic segment problems).
No correction mechanism.

How to measure success

Average rubric score by page type over time, error rates found in audits, time to correct reported errors, and trust signals (links, citations, brand searches) growing.

Editorial QA and E-E-A-T for page systems