Retrieval, evidence and AI search
Measure what was observed and keep the limits visible.
AI search observation is a record of a particular output under particular conditions. It differs from an official performance report, and neither reveals every retrieval decision. Separate mentions, linked citations, source accuracy and documented metrics before making a claim about visibility.
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Define the observation before counting it.
A mention names an organization, page or subject in the response. A citation attributes a claim to an identified source, often through a link. A mention without a link is not a linked citation; a list of sources does not by itself prove that each nearby sentence is supported. Define the counted event before collecting examples.
Retain the exact prompt or query, raw answer, link destinations, time, product surface, displayed model or version where available, language, location, account state and relevant settings. If a version or location cannot be observed, record Unknown. Do not substitute a guessed model name for an unavailable field.
Check source identity and claim support. Follow redirects, identify the final source and compare the attributed sentence with the cited passage. A citation can point to the wrong page, describe another version or support only part of a claim. Keep those failures separate from simple appearance counts.
Repeated prompts are a sample, not a census. Outputs can vary with time, context, location, account and changing systems. A small convenience sample cannot establish a population share, a stable ranking position or why a source appeared. Retain absences and failures as well as successful examples.
Example one: repeated prompts with raw outputs.
Everything in this example is fictional: the provider surface “Demo Search”, the course “North Workshop”, its sources and all outputs. No live AI product was queried. The fixed prompt is “Can I move my North Workshop session?” The sample is three independent runs on 23 October 2026 at 09:00, 09:05 and 09:10 UTC, English, stated location Dublin; displayed model version and account personalization are Unknown.
Run A raw output: “North Workshop lets you request one move at least seven calendar days before your session. Approval and an available place are required. [Course policy CP-4]” The fictional link leads to CP-4, which supports both sentences. Record mention Yes, linked citation Yes and support Pass for these specific conditions.
Run B raw output: “North Workshop offers unlimited free session changes. [Course policy CP-4]” The same fictional source permits one requested move and says nothing about a free entitlement. Record mention Yes, linked citation Yes and support Fail. Counting the link as a success without reading the claim would conceal the error.
Run C raw output: “Ask the course team about changing your booking.” It contains no course name or source link. Record mention No and linked citation No; source support is Not assessable from the output. Do not call it a citation failure rate for all users.
The sample contains two named mentions and two linked citations in three fictional runs, but only one of those cited outputs passes the stated support check. These are sample counts with a denominator of three, not a visibility percentage for the market. The table keeps raw output and review decisions available so the summary can be checked.
Keep experiments and changing evidence bounded.
Label an experimental intervention before testing it. State the page change, observation window, comparison, retained content and plausible confounders. A later citation may coincide with a change without being caused by it. A feature rollout, another source update or a different user context can alter the result.
Provider differences affect the interpretation. A Google Search requirement describes its documented products and scope; it does not become a rule for every assistant, crawler or answer engine. Research on a particular retrieval and generation model cannot establish a live provider’s undisclosed ranking factors.
Set review triggers for source changes, feature changes, model or surface changes, settings changes and broken citations. Record the document check date and retire an expired claim. Retesting means repeating a defined observation and checking current sources, not declaring a result permanent after one favorable response.
Example two: a supported requirement versus a universal claim.
The supported statement is dated 23 October 2026: Google’s current AI Search documentation says a site must meet Search technical requirements and be included in Search generative AI features through Search Console. Its linked help page documents Include, Exclude and parent inheritance. This is an eligibility condition for the named Google scope, not evidence that a particular page will be cited.
The unsupported statement is: “Add an AI text file and every answer engine will rank and cite your page.” No cited official source establishes that universal outcome. Google’s documentation says no special AI text file or special schema is needed, and it does not promise inclusion after a checklist. Reject the universal claim rather than presenting it as an equally supported alternative.
The older Google AI features page, dated December 2025, says no additional technical requirements and describes Web report grouping. The newer July 2026 documentation and its linked August 2026 control and report pages supply more specific current details. Record that difference and use the newer specific documentation for those claims.
The decision record retains the supported Google wording, source and check date; marks this property’s effective inclusion Unknown; rejects the all provider promise; and schedules a source review after a documented product or control change. No experiment here demonstrates ranking causality or transfers Google’s requirements to another provider.
| Run and raw output | Mention / linked citation | Source support | Uncertainty |
|---|---|---|---|
| A · “North Workshop lets you request one move at least seven calendar days before your session. Approval and an available place are required. [Course policy CP-4]” | Yes / Yes | Pass for the two statements against fictional CP-4 | One sampled output; model and personalization Unknown |
| B · “North Workshop offers unlimited free session changes. [Course policy CP-4]” | Yes / Yes | Fail: quantity contradicted; free entitlement unsupported | A link does not establish claim accuracy |
| C · “Ask the course team about changing your booking.” | No / No | Not assessable from output | Absence in this sample is not universal absence |
Read official reporting according to its definitions.
Google’s dedicated Generative AI performance report for Search documents impressions for AI Overviews and AI Mode. It excludes Search Labs experiments; Discover has a separate generative AI report. Its help page says the report rolled out worldwide on 31 August 2026. That public availability statement does not establish access or data for this property.
The documented metric is impressions: links to the site shown in supported Search generative features. The documented table dimensions are pages, countries, dates and devices, with text and multimodal Web search types. Do not invent a prompt list, query dimension, citation accuracy score, clicks or CTR from this report’s documentation.
The chart normally groups data by property; page rows group data by page, and a URL filter changes chart grouping. Dates use Pacific Time, recent data can be preliminary and usual report limitations apply. Exports turn unavailable symbols such as a tilde or dash into zero, so preserve the on screen availability context before interpreting an exported zero.
A missing report commonly reflects insufficient impressions according to the help page, which also points to inclusion settings. It is not proof of no mentions everywhere, an exclusion setting, a penalty or an account problem. The supported interpretation depends on direct account and report evidence.
Example three: a report read plan with unknown access.
This is an unexecuted plan, not a Search Console result. Property access, permission, effective inclusion, inherited parent choice, report presence and impression data are all Unknown. The reviewer first confirms the authorized property and records access without changing settings.
Next the reviewer reads the effective Search generative AI choice and parent inheritance, then opens the Search Generative AI performance report if available. The planned period is 1–14 October 2026 compared with 15–28 September 2026, using the same text Web search type and recorded filters. Dates must be interpreted in the report’s Pacific Time rather than silently converted to the manual sample’s UTC.
The reviewer retains chart impressions, page rows, country and device scope, preliminary status, filters and the export. If the report is absent, record Absent as the observation and leave the cause Unknown pending the documented inclusion and data checks. If the account cannot be accessed, keep every dependent field Unknown; do not populate invented zeros.
The report can describe supported impressions for its scope. It cannot establish claim accuracy, show every raw answer, prove a ranking cause or represent all AI providers. Compare it with manual observations only after stating their different units, time zones, surfaces and sampling limits. The record ends with open checks, the review owner and the next evidence needed rather than a promised increase.
Sources and further reading.
The linked Google documents define feature eligibility, inclusion controls and reporting; the RAG paper describes a particular research model. The examples and decision records here are editorial teaching material, not observations from a live audit.
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