100 guides · Page 2 of 6
AI Search
AI answer engines do not behave like search engines, and the differences are measurable. Across 90 AEOeye audits the same brand was named by Claude in 31% of buyer questions but by Google AI Overviews in only 2% — checking one engine tells you very little.
How to read this section
- 1How each engine sources its answer: live retrieval, training memory, or a blend.
- 2Why the same question returns different brands on different engines.
- 3What causes an answer to change run-to-run, and how to tell drift from a real improvement.
AI Search
AI Search Audit Exclusion Register: Predeclare Every Dropped Run
A practical exclusion register for AI search audits: predeclare eligibility, separate retries from analytic exclusions, preserve evidence, and show denominator effects.
Sep 13, 2026 · 9 min read
AI Search
AI Citation Snippet-Support Rubric: Does the Passage Prove the Claim?
A practical rubric for judging whether an AI citation passage entails a claim, supports it with qualifications, needs context, contradicts it, or cannot be evaluated.
Sep 13, 2026 · 9 min read
AI Search
AI Citation Date Verification: Published, Modified, Effective, or Merely Crawled?
A practical evidence hierarchy and worksheet for verifying publication, revision, effective, crawl, archive, and access dates in AI citation audits.
Sep 13, 2026 · 10 min read
AI Search
AI Brand-Recommendation Rank Coding: Rules for Lists, Prose, and Ties
A deterministic codebook for coding AI brand recommendations across numbered lists, bullets, prose, grouped options, ties, repeats, sponsored blocks, and unordered answers.
Sep 13, 2026 · 9 min read
AI Search
AI Answer Ground-Truth Construction: A Protocol for Time-Bound Claims
A seven-step reference-packet protocol for evaluating time-sensitive AI answers with atomic claims, scoped sources, reviewer disagreement, versioning, and expiry.
Sep 13, 2026 · 9 min read
AI Search
AI Search Source Independence Audit: Trace One Claim to Its Origin
A practical, citation-first method for tracing an AI-search claim to its upstream source, separating syndicated copies from independent corroboration, and documenting the evidence.
Sep 10, 2026 · 7 min read
AI Search
AI Search Sample Size Planning: Prompts, Repeats, and Confidence
A practical, citation-first guide to planning AI search audits with clear sampling units, proportion margin of error, repeat controls, and honest confidence claims.
Sep 10, 2026 · 9 min read
AI Search
AI Search Prompt Order Effects: A Balanced Testing Protocol
A practical protocol for testing whether prompt order changes AI-search answers, with balanced permutations, seeded randomization, duplicate checks, and honest aggregation.
Sep 10, 2026 · 8 min read
AI Search
AI Search Locale Testing Matrix: Language, Region, and Market
A reproducible AI search locale testing matrix for separating language, script, interface, account, location, market, time, engine, model, and prompt effects.
Sep 10, 2026 · 8 min read
AI Search
AI Search Inter-Rater Reliability: A Practical Agreement Guide
A practical guide to measuring reviewer agreement in AI search audits with raw agreement, Cohen's kappa, Fleiss' kappa, and Krippendorff's alpha.
Sep 10, 2026 · 8 min read
AI Search
AI Citation Evidence Preservation: A Reproducible Capture Protocol
A practical, citation-first protocol for capturing AI answers, source pages, timestamps, provenance, hashes, and uncertainty so an AI search audit can be reviewed later.
Sep 10, 2026 · 10 min read
AI Search
AI Citation Accessibility Status Codebook: What Reviewers Can Actually Open
A practical, citation-first codebook for recording whether reviewers can open, read, and verify AI-cited sources without confusing HTTP status with evidence quality.
Sep 10, 2026 · 10 min read
AI Search
AI Brand Entity Resolution: A Codebook for Ambiguous Names
An operational codebook for reconciling AI brand names with canonical domains, legal entities, social IDs, and knowledge identifiers without overstating certainty.
Sep 10, 2026 · 9 min read
AI Search
AI Answer Claim Type Taxonomy: 12 Claims That Need Different Evidence
An operational codebook for classifying claims in AI answers and matching numerical, temporal, causal, comparative, and other claims to the evidence they require.
Sep 10, 2026 · 10 min read
AI Search
AI Search Source Diversity Metrics: Domains, Concentration, and Coverage
A practical reference for measuring source diversity in AI search answers, from unique domains and HHI to entropy, coverage, and cross-engine overlap.
Sep 7, 2026 · 7 min read
AI Search
AI Search Prompt Taxonomy: 24 Buyer Questions to Test
A practical AEOeye framework for sampling branded, unbranded, comparison, risk, implementation, and post-purchase questions in AI search audits.
Sep 7, 2026 · 7 min read
AI Search
AI Search Experiment Reporting Checklist: 24 Fields to Publish
A practical 24-field checklist for publishing reproducible AI search experiments with prompts, runtime context, evidence, coding, uncertainty, and limits.
Sep 7, 2026 · 8 min read
AI Search
AI Content Provenance Standards Directory: What Each Signal Proves
A practical directory comparing C2PA, IPTC metadata, Schema.org, robots.txt, canonical links, and hashes—plus the limits of every provenance signal.
Sep 7, 2026 · 8 min read
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