Yes, LLM guardrails can leverage embeddings for better contextual understanding. Embeddings, which are dense vector representations of words or phrases, help the model understand the meanings and relationships between words in a given context. Guardrails can use these embeddings to detect nuances in language and identify whether content crosses ethical or safety boundaries.
For example, if a user asks a question that involves a complex or ambiguous topic, embeddings help the model understand the intent and context of the request. The guardrails then analyze the contextual meaning to determine if the response may lead to harmful or biased outputs. By using embeddings, the guardrails can more effectively classify and filter content based on deeper understanding, rather than just relying on surface-level keywords.
This advanced use of embeddings makes the guardrails more adaptive and accurate, improving the model's ability to distinguish between safe and harmful content in a wide range of scenarios. This ensures that responses are both relevant and compliant with safety and ethical standards.