"Giskard for LLM QA: Detecting Harmful, Biased, and Broken Behaviors Before Launch"

Large language model applications rarely fail in tidy, benchmark-friendly ways; they fail through prompt injection, hallucination, bias, brittle retrieval, and subtle business-logic breakdowns that emerge only under pressure. This book is written for experienced ML engineers, platform architects, QA leads, and AI governance practitioners who need a rigorous, pre-deployment testing discipline. It positions Giskard not as a demo tool, but as an operational framework for exposing dangerous and costly behaviors before users do.

Across the book, readers learn how to define a practical failure taxonomy, design risk-based evaluation programs, calibrate LLM Scan detectors and thresholds, and test core risk classes such as injection, groundedness failures, fairness defects, and sycophancy. It also delivers a deep treatment of RAG evaluation with synthetic test generation, scenario-based checks, privacy-aware evaluation architecture, and the conversion of one-off findings into reusable regression suites. The result is a complete hardening loop: scan, validate, remediate, retest, and govern.

Rather than offering generic AI safety advice, the book emphasizes decision-grade evidence, release gates, and version-aware operational guidance. Readers should already be comfortable with LLM application architecture, Python-based tooling, and modern software delivery practices; in return, they will gain a disciplined methodology for launching LLM systems with stronger technical confidence and clearer accounta