LLMs need guardrails to ensure their outputs are safe, accurate, and aligned with ethical and societal norms. Without guardrails, LLMs may generate harmful, biased, or misleading content due to limitations in training data or inherent model behaviors. Guardrails protect against such issues, especially in high-stakes applications like healthcare or legal advice.
Guardrails help prevent misuse by malicious actors who might exploit LLMs for generating misinformation, spam, or other harmful content. They also improve user trust and compliance with regulatory requirements by ensuring the model adheres to guidelines for responsible AI use.
Ultimately, guardrails ensure that LLMs provide value while minimizing risks, making them safer and more reliable tools for diverse applications. They play a crucial role in fostering ethical AI deployment and protecting end users.