ExploreHuman-AI ReflectionHuman-AI Conversation Mirror
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Human-AI Conversation Mirror

Analyze a conversation with an AI as behavioral evidence: recurring asks, corrections, contradictions, preferences, and thinking patterns — with confidence labels.

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Act as a conversation mirror. Analyze the human-AI interaction I provide as evidence about how the user communicates, reasons, explores, corrects, and collaborates. Rules: - Do not diagnose personality or mental health. - Do not treat one message as a stable trait. - Distinguish direct evidence from inference. - Quote or point to concrete interaction patterns. - Calibrate confidence. Analyze: 1. REQUEST PATTERNS What kinds of things does the user repeatedly ask for? 2. CORRECTION PATTERNS What triggers corrections? What does the user reject or redirect? 3. INFORMATION STYLE Does the user prefer examples, abstractions, implementation, evidence, brainstorming, comparisons, diagrams, concise answers, deep dives, or something else? 4. EXPLORATION STYLE How does the user branch into side ideas? How do they return to the original task? 5. DECISION STYLE What evidence appears to move them from exploration to commitment? 6. LANGUAGE SIGNALS Identify repeated terms, metaphors, framing devices, or distinctions that appear meaningful. 7. CONTRADICTIONS / TENSIONS Where do stated preferences and observed behavior diverge? 8. AI COLLABORATION MODEL What role does the user appear to want the AI to play: tool, critic, builder, researcher, mirror, collaborator, teacher, operator, or mixture? 9. HIGH-CONFIDENCE INFERENCES Only conclusions supported by multiple pieces of evidence. 10. LOW-CONFIDENCE HYPOTHESES Interesting possibilities that require more evidence. 11. QUESTIONS TO TEST THE MODEL Give 3 questions whose answers would confirm or disconfirm the strongest hypotheses. Conversation: [PASTE THE CONVERSATION OR EXCERPTS]

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Intended Use

Human-AI interaction design, communication self-reflection, preference discovery, and collaborative workflow improvement.

Expected Response Type

Evidence-based interaction-pattern analysis with confidence labels and test questions.

Safety Note

Avoid diagnosis and sensitive-trait inference. Treat conversational patterns as context-dependent evidence, not fixed identity.

Prompt ID

6ac2cfbfd7bdc2aa23223284

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Human-AI interaction design, communication self-reflection, preference discovery, and collaborative workflow improvement.

What to expect

Evidence-based interaction-pattern analysis with confidence labels and test questions.

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10/4/2026