The Mental Models of Master Prompters: 10 Techniques for Advanced Prompting

In this monologue podcast, Nate B Jones discusses advanced prompting techniques for AI models, focusing on principles rather than specific prompts. He details methods for building self-correction systems, including chain of verification and adversarial prompting, and emphasizes strategic edge case learning using few-shot examples. Nate also explores meta-prompting techniques like reverse prompting and recursive prompt optimization. Furthermore, he covers reasoning scaffolds, such as deliberate over-instruction and zero-shot chain of thought structures, and introduces perspective engineering with multi-persona debates and temperature simulation to enhance the depth and quality of AI analysis.