Microsoft Web IQ Evidence Object Audit

Microsoft Web IQ returns structured web evidence for grounded AI applications. Audit the evidence objects separately from Microsoft’s benchmark claims.

Confirmed product fact: Microsoft describes Web IQ as grounding APIs that return structured evidence from web, news, image, and video sources. Its announcement emphasizes passage-level evidence objects that developers can use in AI applications.

Key Moments

Evidence objects are the product Web IQ returns structured passages and source data for grounded applications.
Benchmarks remain vendor claims Treat Microsoft’s published accuracy and latency numbers as attributed results until reproduced.
Audit entailment A relevant source is not enough; the returned passage must support the generated claim.
Preserve the retrieval layer Save evidence objects independently from prompts and final model answers.

Microsoft also publishes performance and quality claims based on its own tests. Those numbers are useful as vendor-reported evidence, not as independently reproduced facts. A defensible evaluation audits the evidence objects first, then tests the complete application under a declared method.

Separate three layers

Do not collapse retrieval and generation
Layer	Question	Evidence to retain
Retrieval	Did Web IQ return useful sources and passages?	Raw evidence objects, query, filters, date
Context assembly	What evidence reached the model?	Selected objects, ordering, truncation
Generation	Did the answer stay inside the evidence?	Prompt, model, output, claim mapping

A fluent answer can hide poor retrieval, and strong retrieval can be damaged by context selection or unsupported generation. Score each layer separately before producing an overall product recommendation.

Audit each evidence object

Record the source URL, publisher, timestamp, content type, passage, and any relevance or confidence field.
Open the canonical source and verify that the passage is present, current, and represented without a misleading boundary.
Classify the source as primary, qualified analysis, reporting, community material, or an uncited summary.
Check whether the passage directly entails the claim or merely shares keywords.
Record duplication, syndicated copies, inaccessible pages, and conflicting sources.

For image and video objects, audit the landing page, media ownership, surrounding context, and whether the result refers to the visual itself or to text near it. A thumbnail is not proof of the claim that a model attaches to it.

Preserve negative evidence too. An authoritative source that was available but not retrieved, a passage that omits a decisive qualification, or a source object that resolves to a different canonical page can explain a weak answer more accurately than a single relevance score.

When the API returns several passages from one publisher, report both object count and unique source-owner count. Ten passages from one document do not provide the same corroboration as independent primary sources, and syndicated copies should not be counted as independent confirmation.

Treat vendor benchmarks as attributed

Microsoft’s announcement describes internal benchmark results, including its query sample and latency claims. The safe wording is “Microsoft reports…” followed by the scope it discloses. Do not convert a vendor test into a universal performance expectation.

Before attempting reproduction, define geography, account tier, API version, query set, cache state, concurrency, network location, timeout policy, and scoring rubric. Measure cold and warm runs separately. Preserve failures and empty evidence sets rather than dropping them from the denominator.

SearchEngineAnswer has not reproduced Microsoft’s Web IQ benchmark. This article provides an audit method and makes no comparative claim against other grounding providers.

Build a claim-level scorecard

Coverage: proportion of material answer claims with at least one evidence object.
Entailment: proportion of cited claims directly supported by the cited passage.
Source ownership: proportion grounded in the source closest to the fact.
Freshness: proportion meeting a predefined date requirement.
Diversity: unique source owners after removing syndication and duplicates.
Latency and reliability: complete distributions, errors, and zero-evidence responses.

Use the SEO and GEO tool-score guide to keep weighted scores transparent. Use the citation-ready passage test to judge whether a final answer can safely travel outside its original context.

Primary announcement

Microsoft Bing: Announcing Microsoft Web IQ

Microsoft Web IQ: How to Audit Evidence Objects Without Trusting Vendor Benchmarks