🎯 Core Goals
- Understand when to trust an LLM and when to verify it.
- Learn that LLMs CAN be used in production — with the right engineering.
LLMs aren’t “all or nothing.” Their reliability depends entirely on the task. Knowing where your task sits on the Trust Spectrum is the single most important judgment call you can make before using an LLM.
👁️ Visuals & Interactives
The AI Trust Spectrum
Click each zone to see which tasks belong there.
check_circle Ready to Use
Creative tasks where variety is a feature, not a bug. The AI excels here.
📝 Key Concepts
- Can you identify whether your task is Ready to use, Review before using, Heavy verification needed, or Don’t trust without engineering?
- A better question: which parts of your project could benefit from LLM use?
LLMs are production-ready — but not for everything. With proper engineering (retrieval, testing, human review), LLMs power systems handling millions of real decisions daily. The risk isn’t that they’re too unreliable to use in serious contexts — it’s using them in serious contexts without the right engineering. Know your zone, and design accordingly.
A useful question before any LLM task: “What’s the worst thing that happens if this response is wrong?” If the answer is “nothing” or “I’d catch it” — go ahead. If the answer is “someone gets hurt” or “we face legal liability” — add engineering before shipping.