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Logical Fallacy Detector & Argument Analyzer
Claude 3.5 SonnetDefensive & Logic

Identifies every logical fallacy in text or arguments with precise labels and fixes.

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All Prompts

Step 1: Customize Prompt Variables

Step 2: Generated Prompt Output

Analyze the following text/argument for logical fallacies and weak reasoning: "Everyone is using AI tools now, so obviously they're better than traditional methods. My friend tried ChatGPT and loves it, proving AI will replace all knowledge workers within 2 years. Anyone who disagrees just fears change and doesn't understand technology." For each problem found: 1. **Fallacy name** (formal name, e.g., Ad Hominem, Hasty Generalization) 2. **Where it appears** (quote the specific sentence) 3. **Why it's a fallacy** (clear 2-sentence explanation) 4. **How to fix it** (what evidence or reasoning would make this valid) Also: - **Overall argument strength**: Weak / Moderate / Strong - **3 unstated assumptions** the argument relies on - **What would actually prove this claim** (gold standard evidence) - **Revised version**: Rewrite the argument without the fallacies
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Comprehensive Technical Analysis & Guide

In-depth breakdown, safety recommendations, and operational mechanics.

1Architecture & Theoretical Basis of this Prompt

This template leverages structural role calibration and negative constraint enforcement. By explicitly scoping the output parameters for Claude 3.5 Sonnet, it reduces token waste and focuses model inference on high-value synthesis.

2Optimizing Temperature & Inference Hyperparameters

For technical coding and code review prompts, set model temperature to 0.1–0.2 for maximum determinism. For creative ideation and artistic prompts, set temperature to 0.7–0.9.

3Chaining Outputs into Production Pipelines

The structured markdown formatting makes it trivial to parse key sections (bugs, recommendations, replacement code) programmatically using standard regex or structured JSON schemas.

4Ethical & Defensive Prompt Practices

Always verify mission-critical code outputs with automated testing suites (unit tests, integration tests) before deploying LLM-generated recommendations into production environments.

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Frequently Asked Questions

Verified answers to common queries regarding this lookup target and security protocols.

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