Large Language Models (LLMs) have become powerful tools for generating text, answering questions, and supporting a wide range of applications. However, despite their impressive capabilities, LLMs have important limitations that users and testers should be aware of.

LLMs can produce information that sounds plausible but is factually incorrect or entirely made up. This is known as “hallucination” and remains a significant challenge, especially in critical or high-stakes applications.

LLMs may reflect or even amplify biases present in their training data. This can lead to outputs that are unfair, discriminatory, or inappropriate, making it essential to monitor and test for bias.

While LLMs can generate human-like responses, they do not truly understand language or concepts. Their outputs are based on patterns in data, not genuine reasoning or comprehension.

LLMs have limits on how much information they can consider at once. They may lose track of context in long conversations or documents, leading to inconsistent or irrelevant responses.

LLMs are susceptible to attacks such as prompt injection or adversarial inputs, where malicious users try to manipulate the model’s behavior or bypass safety measures.

Modern LLMs can process text, images, and other data types, but they may struggle with tasks that require combining information across different formats or acting autonomously in complex environments.

Training and running large models require significant computational resources and energy, raising concerns about efficiency and sustainability.

Due to these limitations, LLMs should be used with human supervision, especially in sensitive or high-risk scenarios. Human review helps ensure outputs are accurate, safe, and appropriate.

The use of LLMs is increasingly subject to regulations and standards (such as the EU AI Act), which may limit how and where these models can be deployed.

LLMs are valuable tools, but they are not perfect. Understanding their limitations is essential for safe, fair, and effective use. Ongoing testing, monitoring, and human oversight are key to addressing these challenges and making the most of what LLMs can offer.