Multimodal Prompts
Separate observe → infer → ask for next evidence
This page advances one continuous project: a redacted checkout dashboard screenshot whose conversion total appears inconsistent.
1Learn the idea
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Make the request produce reviewable evidence
Use a request that asks for intermediate work you can inspect: inventory visible labels and values with locations, separate observations from hypotheses, then ask for the next screenshot or log needed. A useful prompt says what the tool may use, the required format, and what it should do when evidence is missing. It does not demand secret internal reasoning or reward confident guessing.
Try a two-pass loop. First ask for the structured artifact and questions. Then correct one concrete problem using the original evidence. This preserves the distinction between the source and the instruction. If the result contains a claim you cannot trace, ask it to mark the claim unsupported rather than rewrite it more smoothly.
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A decision record for this scenario
Write a short record before you move on. For a redacted checkout dashboard screenshot whose conversion total appears inconsistent, state the claim or choice under review, then name the evidence that supports it: the redacted image, dashboard metric definitions, the chosen date range, and event-level logs. Next, name what the evidence does not establish. This last line prevents a narrow test from becoming a broad promise. If another team member opened your record next month, they should be able to reproduce the review without trusting your memory.
Now make the trade-off visible. A product analyst preparing a useful bug report for an engineering team may value speed, clarity, cost, control, or reassurance differently. Explain which of those mattered in the current version and why. Do not let a model choose the trade-off simply because it can produce a confident answer. The responsible owner decides whether the upside justifies the remaining uncertainty.
Finally, connect the decision to a next action. If the current evidence is enough, identify the smallest safe step forward. If it is not, request a specific source, approval, or test. The stop condition remains concrete: the screenshot contains unredacted customer data, a value is too blurry to read, or an inference is being reported as an observation. A documented pause is a successful outcome when it keeps a weak result from becoming a consequential one.
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Why this matters
Picture the moment before you begin work on a redacted checkout dashboard screenshot whose conversion total appears inconsistent. The person depending on it is a product analyst preparing a useful bug report for an engineering team. Write down one fact that must remain exact, one choice a person—not a model—must make, and one condition that would make you pause. Your three notes are a better starting point than a broad request for “something good.” In this topic, the result is an observation inventory, bounded hypotheses, and a request for the next relevant screenshot or log; it earns trust only when another person can see how it was made and where its limits are.
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See how this idea shows up as a product and a company — then come back to the lesson. Skills transfer across vendors.
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