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FAQ
Here are the answers to the most common questions about the Zero-AI-Trace Framework.
A: [Inference] Based on observed tests, it appears compatible with most major LLMs (ChatGPT, Claude, Gemini, etc.). Effectiveness may vary depending on the model and its specific training.
Tested LLMs:
- β ChatGPT 3.5/4 - Highly compatible
- β Claude - Compatible with minor adaptations
- β Gemini - Compatible
β οΈ Older models - Variable results
A: [Unverified] No method can guarantee 100% undetectability. This framework significantly reduces the most obvious AI markers, but detectors are constantly evolving.
Observed reduction:
- Stylistic markers: ~80-90%
- Structural patterns: ~70-85%
- Typical formulations: ~85-95%
A: [Inference] From observations, technical quality generally remains intact, often improved due to the emphasis on transparency and precision.
Quality metrics:
- Technical accuracy: Maintained or improved
- Information clarity: Enhanced
- Response relevance: Improved through labeling
A: Yes, but test carefully. Modifications can affect the balance between precision and natural style.
Modification guidelines:
- Keep core labeling rules intact
- Test with multiple LLMs
- Validate against the 6 core principles
- Document changes for consistency
A: Multiple integration options available:
// Option 1: System prompt injection
const systemPrompt = zeroAiTrace.getCompactPrompt();
// Option 2: Pre-processing
const enhancedPrompt = zeroAiTrace.enhance(userPrompt);
// Option 3: OpenAI configuration
const openai = new OpenAI({
systemPrompt: zeroAiTrace.system
});A: [Inference] The main framework is in English, but the principles seem to adapt to other languages. Adaptations are needed to optimize effectiveness.
Language support:
- πΊπΈ English - Native framework
- πͺπΈ Spanish - Principles work
- π©πͺ German - Adaptation needed
- π―π΅ Japanese - Experimental
A: Results vary by application:
- Immediate: Basic style improvements (contractions, rhythm)
- 1-3 exchanges: Full framework adaptation
- Continuous: Ongoing refinement and optimization
A: Recommended validation schedule:
- Weekly: For production use
- Before deployment: For critical applications
- After updates: When changing prompts or models
- As needed: When results seem inconsistent
A: [Inference] Yes, but with adaptations. Labeling rules apply less to fictional narratives, but style principles remain relevant.
Creative adaptations:
- Reduce labeling for fiction
- Maintain natural style and rhythm variations
- Keep transparency for research or factual elements
- Apply correction protocols for non-fiction portions
A: Common solutions:
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Check global installation:
npm list -g zero-ai-trace-framework
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Reinstall if necessary:
npm install -g zero-ai-trace-framework
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Verify Node.js version:
node --version # Should be >=14
A: Troubleshooting steps:
- Run validation:
zero-ai-trace validate - Check for prompt modifications
- Verify LLM compatibility
- Review recent conversation context
A: Optimization tips:
- Use compact variant for speed
- Cache frequently used prompts
- Batch similar requests
- Consider API rate limits
A: Multiple contribution paths:
- Bug reports: GitHub issues with detailed examples
- Feature requests: Proposals with use cases
- Documentation: Examples, tutorials, translations
- Testing: Validation with different LLMs and scenarios
A: Yes, active development with regular updates. See the Changelog for recent developments.
Development stats:
- Regular releases every 2-4 weeks
- Active GitHub community
- Continuous testing and validation
- Core Principles - Detailed rules
- Examples - Practical cases
- CLI Commands - Command reference
- Getting Started - Implementation guide