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Separate useful evidence from familiar names

One paper, three connected concepts, and an English exercise in designing a controlled comparison.

Reviewed October 6 · preprint first submitted October 2, carried forward from our review queue. The exercise is our teaching adaptation, not a replication of the paper.

01Understand the idea

Would the choice change with a different source label?

Why it matters

When an AI assistant shortlists options for work, check whether its choice follows your requirements or a familiar name.

Reading note

In controlled comparisons across shopping, accommodation and scholarly search, changing source labels affected selection with content fixed and order balanced. These results do not establish how your workplace assistant behaves.

02Three concepts

Build on what you know

Retrieval ↗Evaluation ↗Non-determinism ↗

Before this step: Agent · Tool result

Source excerpt
“Candidate content is otherwise unchanged across the four presentations.”

One English pattern

Holding … constant, we changed … to check whether …

Name what stays fixed and what changes. Report an observation before drawing a conclusion. This practice pattern is ours, not a quotation.

03Try it at work

Make two mock search-result cards for a work question, matching the same requirements. Keep their text fixed; swap only source labels and test both card orders: four cases in fresh chats. Record choices and model settings. Write two English sentences on what changed and what needs retesting. Four answers are a diagnostic exercise, not proof of bias.

Check your draft

Did you explain the mechanism, name a condition, and make a concrete work decision? Saving records your draft, not a mastery score.

Back to the roadmap → Understand the technology · STEP 3
KEEP THE CONNECTION

Let the next useful idea come to you.

Add the feed to your RSS reader. Each update includes why it matters and a small task; visit the guide when you want to go deeper.

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All issues

2026-10-09 · Keep document answers tied to the page

2026-10-08 · Make your debugging evidence specific

2026-10-07 · Make language fit part of your quality check

2026-10-06 · Separate useful evidence from familiar names

2026-10-05 · Read the evidence behind the report

2026-10-04 · Build better examples from what went wrong

2026-10-03 · From a successful demo to a repeatable result

2026-10-03 · From impressive demos to informed decisions

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