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How do prompts change for multimodal (text+image) models?

Describe what to look at and why: regions, objects, relationships, and the decision to be made. Provide textual context the image can’t show (policy, thresholds). Ask for both a conclusion and a short justification grounded in visible evidence. If you need structured output, define a schema. For safety or compliance, include refusal rules for uncertain cases.

In practice

Point the model’s attention: ‘Focus on the chart’s legend and the rightmost bar; ignore the background text.’

Where to start

  1. [01]Write a template with ‘visual focus’, ‘task’, and ‘output’ sections.
  2. [02]Collect 15 example images with known answers.
  3. [03]Score precision/recall on critical labels.
  4. [04]Add a fallback: escalate if confidence is low.
multimodal promptingimage plus text promptsvision language models

Related questions

// 001How to do prompt engineering for ChatGPT?// 002How does prompt engineering differ across AI models?// 003How should prompts differ for small versus large models?

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Promptise Foundry treats prompts as software — typed blocks, versioning, guards and an inspector that shows you every assembly decision.

Prompt engineering in FoundryRead the guides
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Open source · Python