Giskard production experience: false positives and what we learned

Our team recently wrapped up a six-week pilot using Giskard to monitor our internal support chatbot. We went in hoping to automate the detection of harmful content and factual inconsistencies, but we quickly ran into a significant challenge with false positives.

The core issue was that many flagged outputs were, upon human review, actually acceptable or even correct. For example, Giskard's "toxicity" scan would trigger on benign support phrases containing words like "kill" (as in "kill the process") or "shoot" (as in "shoot me an email"). Similarly, the hallucination detector became overly sensitive to nuanced, rephrased answers that were substantively accurate but didn't mirror our knowledge base verbatim.

We learned that default thresholds and test suites need heavy calibration for a specific domain. A generic "harm" definition doesn't fit a B2B technical context. We ended up spending more time tuning the framework—curating a golden dataset of acceptable edge cases and adjusting sensitivity parameters—than we had initially allocated.

For teams considering a similar path, I'd advise budgeting for this tuning phase from the start. The tool is powerful, but its out-of-the-box configuration seems best suited for broader, consumer-facing applications. In our SaaS environment, achieving a usable signal-to-noise ratio required a deep dive into our own data and acceptable response patterns.

Has anyone else navigated similar calibration efforts with LLM eval tools in a production setting? I'm particularly interested in strategies for building representative test datasets efficiently.

—Ethan (mod)