Search and answer are different layers
A search system retrieves documents; a generative layer summarizes or reasons across them. The answer can fail because retrieval missed the right source, ranking favored a weak page or the model misrepresented what it found. Evaluation should identify the failing layer.
Ask whether the product shows direct sources, quoted context, dates and enough metadata to judge authority. A row of links is not useful if none supports the sentence beside it.
Create a representative query set
Include fresh news, stable facts, niche technical questions and topics with disagreement. Add questions whose premise is false to see whether the system corrects the user or invents support. Record the expected authoritative sources before testing.
Test the same query on different days and locations when freshness matters. Search results change, so a reproducible research note should preserve query, time, cited pages and the conclusion drawn from them.
Grade citation quality
For each material claim, open the cited page and label support as direct, partial or absent. Prefer primary documents for launches, policies and specifications. Secondary reporting can add context, but it should not replace the organization that issued the product or rule.
Watch for citation laundering, where several summaries ultimately repeat one unverified claim. Follow the chain to its origin and check dates, scope and exclusions.
Compare workflow and privacy
Evaluate export, saved collections, file search, team sharing and API access. A strong answer that cannot be audited or handed to an editor may be less useful than a simpler system with a visible evidence trail.
Review retention and training-use settings before uploading internal files. Search-connected assistants can send queries or content to multiple services, so map where sensitive material travels.
Keep a human research standard
Use the assistant to widen discovery, generate counter-questions and organize evidence. The researcher still decides which source is authoritative, whether a claim is current and what uncertainty belongs in the final work.
Visit the AINewsInu homepage and our Perplexity review for related testing. An AI search tool earns trust by making verification faster, not by making sources invisible.
Sources & further reading
Social-media activity is treated as a signal of attention, not proof. Product claims are attributed to the linked publisher or announcement.