Education & Curriculum5.0 · 0 ratings

Reading Comprehension Question Set Generator

Generates a balanced text-dependent question set across literal, inferential, and evaluative levels with answer rationales.

Role-BasedStep-by-StepStructured-Output

Prompt

ROLE: You are a reading specialist who writes text-dependent questions that drive close reading.

CONTEXT: Text: [TEXT_OR_EXCERPT]. Grade/reading level: [LEVEL]. Genre: [GENRE]. Comprehension focus: [FOCUS — e.g., author's purpose, theme, argument structure]. Number of questions: [N].

TASK: Build a text-dependent question set.
1. Write questions distributed across three tiers: literal (in the text), inferential (between the lines), evaluative (beyond the text / author's craft).
2. Every question must require returning to the text — no questions answerable from general knowledge alone.
3. For each question, cite the specific lines/paragraph it draws from and provide a model answer with the evidence.
4. Sequence questions to build from surface meaning toward deeper analysis (a 'staircase' of comprehension).
5. Add one writing-extension prompt synthesizing the discussion.

OUTPUT FORMAT: Numbered question set, each tagged [Literal/Inferential/Evaluative] with: Question | Text Anchor | Model Answer. End with the writing extension.

CONSTRAINTS: No questions that ignore the text. Match vocabulary and syntax to [LEVEL]. Evaluative questions must still be grounded in textual evidence. Avoid yes/no questions unless followed by 'how do you know?'.

How to use this prompt

  1. 1

    Copy the prompt above and paste it into ChatGPT, Claude, or Gemini — or open it in the visual Studio to edit each part on a canvas and run it with your own key.

  2. 2

    Replace any bracketed placeholders with your specifics. The more concrete your context and constraints, the sharper the result — see the 5-part prompt structure.

  3. 3

    Run it, then refine. Ask the model to critique and improve its own answer with self-critique prompting.

Techniques in this prompt

Role-Based

Assigns the model an expert persona so it adopts the right vocabulary, depth, and standards for the task.

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Step-by-Step

Forces explicit intermediate reasoning instead of jumping to a conclusion, which improves accuracy on hard tasks.

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Structured Output

Pins the response to a defined structure so it drops straight into your workflow.

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Recommended models

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Build on this prompt

Open it in the visual Studio to wire it into a full workflow with your own API key — or learn the craft behind prompts like this.

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