The "output validation not just input validation" point is underrated. Most guardrails focus on what goes into the model, but the real risk is what comes out and gets executed.
We're working on similar problems at keypost.ai - policy enforcement at the tool-calling boundary for MCP pipelines. Different angle (we're more focused on access control and rate limits than hallucination detection per se) but same philosophy of deterministic enforcement.
Question: how do you handle the tension between semantic enforcement and false positives? In our experience, too-strict semantic rules block legitimate use cases, but too-loose lets things through. Any patterns that worked for calibrating that?
Comments
The "output validation not just input validation" point is underrated. Most guardrails focus on what goes into the model, but the real risk is what comes out and gets executed.
We're working on similar problems at keypost.ai - policy enforcement at the tool-calling boundary for MCP pipelines. Different angle (we're more focused on access control and rate limits than hallucination detection per se) but same philosophy of deterministic enforcement.
Question: how do you handle the tension between semantic enforcement and false positives? In our experience, too-strict semantic rules block legitimate use cases, but too-loose lets things through. Any patterns that worked for calibrating that?