What's really happening when your prompts are either too detailed or not detailed enough? The common story is that more clarity always helps, but the reality is more complicated when over-specifying kills creativity and burns context just as badly as under-prompting does. In this video, I share the inside scoop on finding the right altitude for LLM prompts:

Why over-specifying crushes model judgment and wastes the context window you actually need
How under-prompting forces large language models to guess in ways that compound downstream
What Goldilocks prompting unlocks in Claude, GPT-5, and Gemini when you hit the right level of detail
Where short, reusable prompt slugs outperform long instruction dumps for operators building at scale

For operators and teams navigating 2026, a balanced prompting strategy gives you more control without surrendering the model judgment that makes AI worth using in the first place.

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