Data contract for Bing grounding queries: fields, freshness, ownership and QA

Short answer: Treat Bing grounding queries as sampled retrieval-context evidence, not as a complete keyword dataset. A safe data contract should record the phrase, observation window, associated cited-page context, source system, extraction date, sampling limitation, owner and review state. Never convert grounding-query samples into search volume, ranking position or guaranteed content demand.

What a grounding query represents

Microsoft describes grounding queries in AI Performance as key phrases used when retrieving content that was referenced in AI-generated answers. The dashboard explicitly says the data represents a sample of overall citation activity.

That definition is the center of the data contract.

A grounding query is therefore not automatically:

• a user-entered query;
• a search-volume metric;
• a rank position;
• a full transcript of the answer;
• a guarantee that one page caused the final response.

The contract should preserve that scope wherever the data is exported or transformed.

Required field: source system

Every row should identify the producing system, such as:

source_system = Bing Webmaster Tools / AI Performance

This prevents grounding-query samples from being mixed with:

• Bing Search keyword data;
• Google Search Console queries;
• internal site search;
• paid-search terms;
• prompt logs from manual answer-engine tests.

Different sources can use similar strings while representing different events.

Required field: observation window

Store the selected reporting period and the extraction timestamp.

Useful fields include:

• window_start;
• window_end;
• observed_at;
• timezone_or_reporting_context where relevant.

Without a time boundary, the same phrase can be misread as permanently active demand.

Required field: query phrase

Preserve the phrase as reported instead of aggressively normalizing it.

You may create a secondary normalized field for clustering, but keep the raw value for auditability.

Suggested fields:

• grounding_query_raw;
• grounding_query_normalized;
• cluster_id;
• normalization_rule_version.

Normalization should never overwrite the raw evidence.

Required field: cited-page context

Where the reporting workflow allows a relationship to cited pages, record it carefully.

Possible fields:

• page_url;
• page_cluster;
• citation_activity_context;
• page_observed_in_period.

Do not invent a one-to-one relation if the export or interface does not provide one.

If the association is inferred manually, label it as inferred.

Required field: sampling status

Microsoft explicitly describes grounding queries as a sample.

Add a field such as:

sampled_metric = true

and a note:

coverage_unknown = true

This protects downstream analysts from treating the dataset as exhaustive.

Freshness rules

Grounding-query data is fast-changing evidence.

A sensible freshness model includes:

• extraction date;
• latest verified platform definition;
• whether the query remains observed in a newer period;
• whether the related cited page changed materially;
• whether Microsoft changed the metric definition.

Do not keep a phrase in a permanent strategy table without a last-observed date.

Ownership

Assign owners for different layers:

• SEO/AEO analyst: interpretation and clustering;
• data owner: extraction and schema integrity;
• editor: content decision;
• technical owner: crawl/index diagnosis;
• measurement owner: causal or business interpretation.

The analyst who sees a phrase should not automatically create a new article from it.

QA checks

A useful QA routine checks:

• missing raw phrase;
• missing observation window;
• malformed URL;
• duplicate export rows;
• normalization collisions;
• stale snapshots;
• inferred page associations mislabeled as direct;
• queries promoted to content briefs without intent review.

QA should verify both data quality and interpretation quality.

Safe uses

Grounding-query samples can help with:

• identifying observed retrieval contexts;
• finding pages already participating in AI answers;
• checking whether page scope matches the retrieval phrase;
• discovering questions worth deeper research;
• prioritizing factual refreshes;
• comparing topic clusters descriptively.

These are discovery and diagnostic uses.

Unsafe uses

Avoid using the dataset as:

• keyword volume;
• universal AI demand share;
• rank tracker;
• automatic publishing queue;
• authority score;
• revenue forecast.

Those conclusions require different evidence.

Example contract states

Use states such as:

• OBSERVED_SAMPLE;
• NORMALIZED;
• CLUSTERED;
• PAGE_CONTEXT_VERIFIED;
• CONTENT_REVIEW_REQUIRED;
• NO_ACTION;
• STALE;
• DEFINITION_CHANGED.

The states make the lifecycle visible.

Validation cadence

Revalidate the contract whenever Microsoft changes the AI Performance definition, export behavior or supported surfaces, and whenever your own normalization or clustering logic changes. Preserve old snapshots so analysts can distinguish a real retrieval-pattern shift from a schema or processing change. If a grounding phrase disappears, mark it as no longer observed rather than deleting its history; the historical state can still explain an editorial decision made during the earlier observation window.

The data-contract rule

Grounding queries are valuable because they expose sampled retrieval context from AI-generated answers.

Keep the raw evidence, preserve the sampling limitation, attach time and ownership, and require intent review before the data becomes an editorial action.

Sources reviewed

• https://blogs.bing.com/webmaster/February-2026/Introducing-AI-Performance-in-Bing-Webmaster-Tools-Public-Preview