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.
Hacker News (AI / LLM) files mainly under ai. We currently carry 2,396 of its stories.
Story provenance
Source · retrieval · rights · ranking — open for full record
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Attribution is not the same as permission. This drawer separates discovery metadata, excerpts, WeSearch-generated summaries, reuse status, and whether the publisher receives the visit. Nothing here claims a legal grant the publisher has not made.
Record
| Original publisher | GitHub |
| Canonical URL | https://github.com/AssimilatedHuman/LLM-Inquisitor |
| Publication time | Wed, 20 May 2026 13:24:32 +0000 |
| Retrieval time | 2026-05-20T13:35:02.606Z |
| Last seen | 2026-05-20T13:35:02.606Z |
| Headline source | Publisher (no WeSearch rewrite) |
| Excerpt source | publisher body |
| Excerpt method | First ~120 words (~800 chars) of extracted publisher body, fair-use limited. |
| Summary | WeSearch · cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
| Citation coverage | Summary is a WeSearch-generated derivative; primary citation is the original publisher URL. |
| Cluster | oIh_K87ftAuL |
| Cluster logic | Grouped by semantic title/content similarity across sources within a rolling window. Same-publisher template collisions are excluded from coverage comparison. |
| Ranking reason | Story pages are not engagement-ranked. Hub feeds use recency, with optional source-diversified chronological ordering (cap consecutive stories per source). No personalized ranking. |
| Publisher visit | Yes — open original |
| Substitutes article? | No — link-out required for full text |
Rights status (four layers)
WeSearch handling by dimension
| Indexing | May the item be indexed (stored, ranked, made findable)? | Allowed |
| Snippet | May a short excerpt of the publisher's text be shown? | Allowed |
| AI summary | May WeSearch generate its own short summary of the article? | Limited |
| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
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.