Appendix: References
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Wei et al., 2022. “Chain of Thought Prompting Elicits Reasoning in Large Language Models”
Relevance: Forms the backbone of your structured prompting techniques like MARS, GROWS, and CRITIC. CoT prompts are foundational for steering AI toward better reasoning.
Used in: Chapters 4 (Beyond Prompting), Chapter 5 (Ghosted by the Machine), Chapter 14 (Session Reflection)
https://arxiv.org/abs/2201.11903 -
Gao et al., 2023. “A Strategic Coordination Framework of Small LLMs Matches Large LLMs in Data Synthesis” (GRA framework)
Relevance: Inspired your use of modular AI roles and prompting strategies like “Council Review.” Highlights the value of coordinating smaller reasoning loops.
Used in: Chapter 9 (Council of Purpose), Chapter 14 (MIRROR), Chapter 15 (When It Doesn’t Work)
https://arxiv.org/abs/2309.10685 -
Zelikman et al., 2022. “STaR: Bootstrapping Reasoning With Reasoning”
Relevance: Related to your self-improvement loops like GROWS, where the model reflects, scores, and iterates toward a better result.
Used in: Chapters 4, 5, and 13 (Machine Coding)
https://arxiv.org/abs/2203.14465 -
Wu et al., 2024. “xVerify: Efficient Answer Verifier for Reasoning”
Relevance: Supports your focus on critique-first prompting and reflective loops like MIRROR and CRITIC.
Used in: Chapters 10 (Second Pass), Chapter 14 (Reflection), and Appendix on Prompting Tools
https://arxiv.org/abs/2402.08851
