LLMs have several limitations in generating responses, such as producing plausible-sounding but factually incorrect or irrelevant outputs. This occurs because they rely on patterns in their training data rather than actual understanding or reasoning. For instance, an LLM might confidently generate a wrong answer to a technical question.
Another limitation is sensitivity to poorly phrased or ambiguous prompts. If a query is unclear, the LLM might misinterpret it and provide an incorrect or unrelated response. Additionally, LLMs sometimes lack specificity, offering generic answers when more detailed information is needed.
LLMs also struggle with maintaining consistency in extended conversations, as they lack memory of prior interactions beyond their input context. This makes it difficult to handle long dialogues or tasks requiring sustained focus. Developers can mitigate these issues by improving prompt engineering, using external tools for fact-checking, or designing workflows that manage context better.