LLM INQUISITOR: Evaluating how AI models handle long, realistic tasks
LLM INQUISITOR is a methodology designed to evaluate AI systems in real-world scenarios rather than controlled environments. It aims to identify issues such as instability and unpredictability during normal workflows. The tool is intended for developers, engineers, and analysts who require reliable AI behavior in practical applications.
- ▪LLM INQUISITOR provides a practical approach to assess AI behavior during actual use.
- ▪The methodology helps identify failures that occur in real tasks, such as coding sessions and customer interactions.
- ▪It includes resources like a Quick Start Guide and a Practitioner’s Guide for effective evaluation.
Opening excerpt (first ~120 words) tap to expand
LLM INQUISITOR — GitHub Edition The Behavioural Evaluation Standard for Real‑World AI LLM INQUISITOR is a practical, workflow‑driven methodology for evaluating how AI systems behave when they’re actually used — not when they’re demoed, benchmarked, or prompt‑engineered. If you want to know whether an AI is stable, reliable, predictable, and safe in real work, INQUISITOR is the tool. Why INQUISITOR Exists AI doesn’t fail in benchmarks. It fails in: developer workflows document editing analysis tasks coding sessions customer‑facing interactions That’s where drift, collapse, contradiction, contamination, and instability actually matter. INQUISITOR reveals that behaviour using normal work, not adversarial tricks.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at GitHub.