# Test AI Asset Search with Known Asset Examples

> Evaluate asset discovery with a small set of known examples, expected exclusions and reviewed results before relying on natural-language catalog search.

Evaluate asset discovery with a small set of known examples, expected exclusions and reviewed results before relying on natural-language catalog search.

Test asset search by starting with a real catalog task and files whose intended roles are already known. Write queries, expected asset IDs and exclusions before searching. Then record what the selected workspace actually returns. This gives the owner evidence about one retrieval task without claiming to measure the entire catalog.

A visually similar result can still be wrong. The user might need the approved interior image for a campaign, not an earlier draft of the same scene. The acceptance packet must contain supplied version, status and permission facts; search relevance cannot establish them by itself.

## Define the task and baseline

Name the user role, workspace, collection, catalog version and task. For example: “A marketer must find the approved refill-station interior image for a brochure.” Record which permission context will be used during testing and who supplied the asset status. Do not change permissions or grant access to make a missing expected result appear.

Freeze a small known-example set. Include each asset’s ID, version, approved status where supplied, descriptive evidence and its role in the test. Build the packet from actual owner records rather than asking the search system to decide which file should be expected. Keep out-of-scope files separate from known exclusions.

Use [reviewed digital asset metadata](/guides/marketing/ai-digital-asset-metadata) to resolve conflicting tags before interpreting search findings. [Digital asset management](/glossary/digital-asset-management) includes context needed to select a file, not just the visible subject. Keep the source of that context in the test packet.

## Write known-result cases

The following catalog and outcomes are fictional. The owner supplies these records:

| Asset | Supplied context | Test role |
| --- | --- | --- |
| IMG-17 | Interior refill-station image, approved for the brochure | Expected asset |
| IMG-09 | Earlier refill-station image, obsolete | Explicit exclusion |
| IMG-22 | Similar interior scene, draft without brochure approval | Excluded for approved-campaign use |

Create one discovery case using the query “refill station interior,” with IMG-17 expected. Record IMG-09 as obsolete and IMG-22 as a draft. Create a separate approved-campaign case using the actual query and filters the marketer intends to use. Do not assume a natural-language query automatically applies approval or permission rules.

For every case, write query text, filter settings, expected asset IDs, prohibited selections, observation scope and the acceptance rule. The owner might require the expected asset to appear in the first visible result screen and the selected file to have the supplied approved version. That is a local criterion for this task, not a general ranking guarantee.

Include a deliberate negative case only when its expected absence is known. “Nothing useful should appear” is too vague to review. State what would count as an incorrect return or an incorrect selection and why the source records support that judgment.

## Run search in the approved workspace

[Bynder's product page](https://www.bynder.com/en/products/digital-asset-management/) describes AI discovery including natural-language, image and similarity search. The [Bynder profile](/tools/bynder) offers a proposed pilot. Test only the supported operation available in the chosen account and record the search mode alongside the query.

Execute each case manually using the specified role and filters. Record the date, catalog version, visible result order, expected asset position or absence, excluded results and the file actually opened or selected. Keep the query unchanged during the first pass. If you experiment with another phrase afterward, save it as a new case rather than replacing the failed original.

For the fictional discovery query, suppose IMG-22 appears before IMG-17. That observation may still help a person discover the subject, but it does not meet an approved-campaign selection criterion if the user chooses the draft. Record the exact distinction. Do not report that the system found the right asset simply because the first result looked suitable.

## Diagnose without overstating the result

Classify each failure by what the evidence supports. The expected asset might have conflicting metadata, the chosen filter might exclude it, the test role might lack supplied access, or the search might return an unsuitable ordering. Record these as separate hypotheses and checks. A single missed query does not tell you which component caused the result.

Use the [asset metadata review ledger](/skills/marketing/asset-metadata-review-ledger) to propose a correction when evidence points to a tag problem. For IMG-17, a filename containing outdoor should not override a supplied indoor inspection note. Preserve the owner’s decision and original value before applying an approved change.

If the permission context or approval record is inconsistent, send that issue to its owner rather than editing descriptive tags until the result appears. An access problem and a vocabulary problem require different decisions. Search testing should expose the mismatch, not disguise it.

## Compare an approved metadata change

Rerun the same case set after the owner approves a bounded metadata update. Keep query text, filters, user role and observation scope fixed. Record the new catalog version and accepted change IDs. This comparison shows what changed under the stated conditions; it does not isolate every possible cause if the catalog or account changed in other ways.

For the fictional packet, the owner may correct IMG-17’s setting tag to indoor. A later appearance of IMG-17 on the first visible screen is a useful observed improvement for that case. It is not a claim about global recall, every synonymous query or all approved assets. Preserve unsuccessful cases too, so the review record does not become a curated list of wins.

## Make a bounded acceptance decision

Deliver the case packet, baseline observations, approved changes, repeat observations and unresolved issues. The owner should decide whether the workflow is useful for the named task, which limitations users must understand, and whether more cases are needed before broader adoption.

Acceptance might cover finding IMG-17 with the checked role and query while still requiring a manual approval-status check before reuse. That is a meaningful outcome. Keep the selected asset’s version and supplied status in the final handoff. An attractive search result is a candidate; the source owner’s records determine whether it is the intended asset for the job.

## Sources and further reading

- [Bynder Digital Asset Management](https://www.bynder.com/en/products/digital-asset-management/) — Bynder

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_Source: https://agentscamp.com/guides/marketing/ai-asset-search-acceptance-test — Guide on AgentsCamp._