Guardrails are not limited to specific types of LLMs; they are essential for all language models, regardless of size or application domain. However, the design and implementation of guardrails may vary depending on the model's use case, such as customer support, medical advice, or creative writing.
For smaller, domain-specific LLMs, guardrails may focus on ensuring accurate and relevant outputs within a narrow scope. For larger, general-purpose LLMs, guardrails need to address a broader range of risks, including bias, harmful content, and hallucinations.
While the principles of guardrail implementation remain consistent, their customization ensures alignment with the specific goals and challenges associated with each LLM deployment scenario. This adaptability is crucial for maintaining safety and usability across diverse applications.