# What LLMs Are Good and Bad At — Large Language Models

Source: https://www.geekswithgeeks.com/en/llms/f-limits

> Know the strengths and typical failure modes.

## Fluent is not the same as correct

LLMs are strong at **language tasks**: summarising, rewriting, translating, classifying, extracting fields, explaining, drafting code and following examples. Typical weaknesses: **hallucination** (confident but invented facts or citations), **knowledge cutoff** (nothing after training unless supplied), **unreliable exact arithmetic and counting**, **inconsistency** between runs, **sensitivity to wording**, and **limited memory** (only what fits in the context window). The practical rule: use LLMs where a wrong answer is cheap to detect or can be verified, and add retrieval, tools and checks where it is not.

## Verify what you cannot afford to get wrong

Ask yourself: if this answer is wrong, who is hurt and how fast would I notice? If the answer is "badly and slowly", add a check or a human.

**Quiz:** What is hallucination?

- [ ] A GPU overheating
- [x] Confident output that is false or invented
- [ ] A slow network
- [ ] Compressing a prompt

*Answer:* Confident output that is false or invented. The model generates plausible text, not verified facts.