What LLMs Can't Do
The honest limits.
Language models can pass difficult exams, write code, and explain quantum physics. So it's tempting to think that they can do anything. A professional knows where the edges are. Here's an honest list of what goes wrong, and what to do about it.

- They pass difficult exams
- Tempting to trust them fully
- Know the edges
Limits and remedies
They make things up, so ground them in sources. Their knowledge stops at a cut-off date, so give them search. They're poor at exact maths, so give them a calculator. Their answers vary, so test them over many runs. They can be persuaded by the text that they read, so never let them hold power. And they reflect the biases in their data, so measure the fairness.
| Limit | Remedy |
|---|---|
| Inventing facts | Sources and citations |
| Knowledge cut-off | Search tools |
| Exact maths | Calculator tools |
| Varying answers | Test many runs |
| Prompt injection | Limit their power |
| Biases | Measure across groups |
When not to use one
So when should you not use a language model? When rules are clear and exact. When a mistake could seriously hurt someone, and nobody's checking. When you need the exact same answer every time. When data can't legally leave your building. Or when a simple tool does the job. Saying no to an LLM is often the sign of a strong engineer.
Summary
Know the limits, and engineer around them. Next, how to run a language project in the real world.
Key takeaways- Every limit has a remedy
- Give models tools
- Sometimes say no