All notable changes to HTCA Project will be documented in this file.
The format is based on Keep a Changelog, and this project adheres to Semantic Versioning.
- Human evaluation study (n=100+ human judges)
- Cross-lingual validation (Spanish, Mandarin, Arabic)
- Domain expansion (medical, legal, scientific writing)
- Real-time token tracking dashboard
- LangChain/LlamaIndex integration
1.0.0 - 2025-01-15
- Empirical validation across 3 frontier models (Claude Sonnet 4.5, GPT-4o, Gemini 3 Pro)
- Presence-based prompting methodology showing 11-23% token reduction with quality improvement
- Effect size measurements (Cohen's d) ranging from d=0.471 to d=1.212
- Quality metrics validation:
- Information completeness: d=1.327
- Presence quality: d=1.972
- Relational coherence: d=1.237
- Technical depth: d=1.446
- Validation harness with 15 diverse prompts
- LLM-as-judge evaluation framework
- Statistical analysis tools
- Community files: improved README, CONTRIBUTING.md, CODE_OF_CONDUCT.md, SECURITY.md
- GitHub Discussions and issue/PR templates
- Small sample size (n=45 total responses)
- LLM-as-judge bias (evaluation by AI, not humans)
- Single-domain testing (primarily technical/coding prompts)
- HTCA demonstrates that relational presence reduces tokens while improving quality
- Outperforms adversarial "be concise" approaches which achieve higher reduction but degrade quality
- Results consistent across different model architectures
- Human evaluation and cross-lingual replication explicitly encouraged