I've been testing every major AI assistant over the past year, and there's a consistent pattern: they prioritize confident-sounding brevity over actual depth.

Ask a complex question and you'll get a clean 3-paragraph summary. Useful, but nowhere near the depth a researcher, developer, or analyst actually needs.

The problem isn't the model — it's the design philosophy. Most AI tools are optimized for perceived clarity, not informational density.

Here's what changes when you prompt for depth instead:

1. Ask for underlying mechanisms, not just outcomes

Instead of "how does X work", ask "what are the failure modes of X and why do they occur" — forces the model to reason through edge cases.

2. Layer your context

Don't ask a single question. Give the model your full context first — what you know, what you're trying to decide, what you've already tried. You'll get answers calibrated to your actual situation.

3. Request explicit uncertainty

Ask the model to flag where it's confident vs. uncertain. Most models will bury uncertainty in confident-sounding language by default.

That's the whole premise behind InfoCore AI — built to give you the unabridged version, not the Wikipedia summary.