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).
LLM-based semantic checks catch more of these issues, but introduce real trade-offs around latency, cost, and operational complexity.
We’ve ended up with a hybrid approach (rules first, semantic checks second), but it still feels like a moving target as systems scale.
Some specific questions:
Where have deterministic rules clearly failed you in production?
What types of checks have you found must be semantic?
What do you deliberately avoid letting an LLM decide?
Any non-obvious failure modes you only discovered after shipping?
While exploring this space, we’ve also built internal tooling around guardrails and data security, but the main goal here is learning from others who’ve shipped and operated LLM systems at scale.
Would love to hear real-world experiences.