The AI Prompt Debugging Guide

I’ll admit I rolled my eyes at first—another AI book? But *The AI Prompt Debugging Guide* won me over by addressing the elephant in the room: most prompts suck, and it’s not always the model’s fault. Sullivan’s approach is refreshingly hands-on, like sitting beside a seasoned mechanic while they diagnose an engine misfire. The ‘red teaming’ chapter, where he deliberately breaks prompts to stress-test their robustness, was a revelation. I immediately applied his ‘context layering’ technique to a client project and cut my revision time in half. The narration, via Jon Mills’s voice replica, is crisp and well-paced, though I occasionally wished for more vocal emphasis during complex examples—a minor quibble, but in dense sections (like the token-efficiency deep dive), a human narrator might’ve added nuance with pauses or tone shifts. The production quality is flawless, though purists might balk at the AI narration; personally, I found it fitting for a book about debugging AI. My only real critique? The final chapter’s ‘future-proofing’ advice feels slightly optimistic given how fast LLMs evolve. Still, this is the rare tech audiobook that justifies its runtime with zero wasted words.