Chain-of-Density (CoD) iteratively refines verbose content into concise yet informationally rich abstracts, like distilling wine to its essence. Starting with a full paraphrase, each chain link identifies and incorporates overlooked key entities (names, dates, concepts) while pruning redundancies, aiming for maximal density—measured by entities per sentence—without sacrificing fidelity. This graduated compression is perfect for executive digests, academic abstracts, or knowledge extraction, balancing brevity with completeness.
Examples
User Prompt: Summarize a news article on AI regulations using CoD. AI Response: Chain 1: EU proposes AI Act for high-risk systems. Chain 2: Act targets facial recognition, mandates audits; Entities: EU, AI Act, 2026 enforcement. Final: Dense: EU AI Act (2026) regulates high-risk tech like biometrics via transparency audits.
User Prompt: Abstract a scientific paper on gene editing using CoD. AI Response: Chain 1: CRISPR advances. Chain 2: Off-target effects minimized; Key: Cas9 variants, 95% accuracy. Final: CRISPR-Cas9 refinements cut off-target edits to <5%, enabling precise therapeutic applications.
User Prompt: TL;DR a financial report using CoD. AI Response: Chain 1: Q3 earnings overview. Chain 2: Revenue $1.2B, up 15%; Entities: TechCo, EBITDA margin 30%. Final: TechCo Q3: $1.2B revenue (+15%), 30% EBITDA amid supply chain wins.
User Prompt: Condense a book review using CoD. AI Response: Chain 1: Plot and themes. Chain 2: Strengths in character arc; Entities: Author X, dystopian setting. Final: X's dystopia shines via flawed hero's redemption, though pacing lags mid-act.
Advantages
- Achieves succinctness without entity loss, ideal for time-strapped readers.
- Systematic density buildup ensures progressive informativeness over crude cuts.
- Supports knowledge graph construction by highlighting core nouns and relations.
- Applicable to vast corpora, streamlining literature reviews or intel briefs.