Giskard production experience: false positives and what we learned

Having conducted a rigorous evaluation of several LLM testing frameworks for our production-grade customer support agent, our team's experience with Giskard has been instructive, particularly regarding its propensity for false positive alerts. While the framework's ambition to automate vulnerability scanning for LLMs is commendable, our deployment revealed significant noise that required substantial tuning to align with our actual risk profile.

Our primary use case involved testing a RAG pipeline against a suite of custom domain-specific prompts. We immediately observed that the default thresholds for tests like **"reproducibility"** and **"stereotype"** detection were far too sensitive for our context. For instance, minor, semantically inconsequential variations in LLM output (e.g., "The document states..." vs. "According to the provided text...") would trigger a reproducibility failure. Similarly, the stereotype tests would flag benign demographic references present in our source knowledge base as potential harms, without evaluating the neutral or protective context.

We were forced to adopt a highly iterative calibration process:

* **Benchmark-Driven Threshold Adjustment:** We first ran Giskard against a golden dataset of known-good Q&A pairs from our production logs. We then adjusted detection thresholds (e.g., `threshold=0.98` for reproducibility) until the framework's results matched our manual assessment. This baseline became our project-specific configuration.
* **Custom Metric Development:** The out-of-the-box "toxicity" and "harmfulness" metrics, often backed by another LLM-as-a-judge, proved unreliable. We supplemented them with a suite of custom, rule-based checks using Giskard's `@metatest` decorator to scan for specific, high-risk policy violations that we had historically encountered.
* **Pipeline Stage Gating:** We learned not to run the full battery of tests at every stage. We integrated a subset of deterministic, rule-based tests into our CI/CD, while reserving the more stochastic and sensitive scans (like hallucination detection) for nightly regression suites, where a human-in-the-loop could triage alerts.

Here is a snippet of our final test configuration for the CI pipeline, demonstrating the heightened thresholds and focused scope:

```python
from giskard import test, scan, Dataset, Model

# Load our fine-tuned model and a curated validation dataset
model = Model(my_llm_model, model_type="text_generation")
dataset = Dataset(pd.read_csv('validation_safe_prompts.csv'))

# Execute a tailored scan with adjusted parameters
scan_results = scan(
model,
dataset,
only=["reproducibility", "output_standardization"],
thresholds={
'reproducibility': 0.97,
'output_standardization': 0.95
}
)

# Custom test for a critical business rule
@test(name="no_pii_reference")
def test_no_pii_in_output(model, dataset):
# Custom logic to check for SSN, email patterns
...
```

**Key Learnings:** Giskard functions best not as an out-of-the-box guardrail but as a extensible testing substrate. Its default settings appear calibrated for the most general and high-risk public-facing chatbot scenarios, creating substantial false positive overhead for specialized enterprise applications. The value is unlocked only after significant investment in baseline calibration and custom test authorship. The framework's architecture is solid, but teams must be prepared to treat its initial findings as a starting point for investigation, not a definitive verdict.

For teams considering adoption, I recommend a phased approach: begin with a proof-of-concept using your most critical and well-understood prompts, quantify the false positive rate, and budget for the necessary customization effort. How have others navigated the balance between automated detection sensitivity and operational overhead in their LLM evaluation suites?

— Isabella G.