Learning Objectives
Define discourse coherence and its major components: reference chains, connectives, and topic continuity
Analyze Rhetorical Structure Theory (RST) and its practical applications in AI text generation
Evaluate LLM coherence failures systematically using formal discourse structure analysis
Apply coherence principles to design better long-form AI content generation workflows
How texts achieve coherence — and why LLMs sometimes produce fluent but ultimately incoherent extended text.
This lesson builds on the theoretical foundations established earlier in the module and extends your understanding of how cognitive-linguistic principles apply directly to modern AI systems.
Learning Objectives
Define discourse coherence and its major components: reference chains, connectives, and topic continuity
Analyze Rhetorical Structure Theory (RST) and its practical applications in AI text generation
Evaluate LLM coherence failures systematically using formal discourse structure analysis
Apply coherence principles to design better long-form AI content generation workflows
Key Concepts
Define discourse coherence and its major components: reference chains, connectives, and topic continuity
Analyze Rhetorical Structure Theory (RST) and its practical applications in AI text generation
Evaluate LLM coherence failures systematically using formal discourse structure analysis
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