Where do deterministic rules fail for LLM guardrails?
Large Language Modelsguardrailsdeterministic rules
For those running LLMs in production, I’m curious where you’ve seen deterministic rules (regex, allowlists, schema validation, etc.) start to fall apart when used as guardrails. In our experience, rule-based checks are fast, cheap, and predictable, but they struggle with context, intent, and edge cases (e.g. indirect PII leaks, policy violations expressed semantically, or “valid” JSON that’s still wrong).
Synthesized Answer
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Deterministic rules often break down when dealing with complex, nuanced, or context-dependent scenarios. For instance, detecting indirect PII leaks or policy violations that are expressed semantically rather than through explicit keywords can be challenging. Additionally, rule-based systems may struggle with edge cases, such as 'valid' JSON that is still semantically incorrect. To address these limitations, many teams adopt a hybrid approach, using deterministic rules as a first line of defense and supplementing them with LLM-based semantic checks.
Key Takeaways
Deterministic rules struggle with context and intent
LLM-based semantic checks can catch nuanced issues
Hybrid approaches can balance speed and accuracy
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