Episode Details

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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© Nate B. Jones 2026

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