Navigating the Evolution of Prompt Engineering
summary
Argues that prompt engineering behaves like a flow system and proposes using Constructal Theory to explain why prompting patterns evolve from zero‑shot to chain-of-thought, to tree-of-thought and self-consistency (tree backtracking reduces logical failures). Links thermodynamics to diffusion models (DALL·E, MidJourney, Stable Diffusion) and shows LLM sizes follow a power‑law so both a few giant models and many small specialists persist. Cites research that imperfect code from ChatGPT/GitHub Copilot still speeds developers despite bugs.
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chapters (6)
- 0:00Introduction and motivation
- 1:04Definition and theoretical framework
- 8:01Evolution of prompting patterns
- 12:44Size evolution of LLMs
- 16:56AI-generated software programs
- 19:46Takeaways and conclusion
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