GitHub - AssimilatedHuman/LLM-Inquisitor: Evaluating AI behaviour under real‑world work conditions to surface issues before they become problems. LLM INQUISITOR identifies failures (drift, instability etc) by observing AI during normal tasks — a tool the industry desperately needs to stem the 85% failure rate. Includes Quick Start, Practitioner’s Guide and Methodology. · GitHub
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LLM-Inquisitor
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LICENSE
LICENSE
LLM INQUISITOR 1 Page User Report Aid.pdf
LLM INQUISITOR 1 Page User Report Aid.pdf
LLM INQUISITOR Methodology (GitHub Edition) V1.pdf
LLM INQUISITOR Methodology (GitHub Edition) V1.pdf
LLM INQUISITOR Practitioners Guide V 1.pdf
LLM INQUISITOR Practitioners Guide V 1.pdf
LLM INQUISITOR Quick Start Guide V 1.pdf
LLM INQUISITOR Quick Start Guide V 1.pdf
README.md
README.md
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LLM INQUISITOR — GitHub Edition<br>The Behavioural Evaluation Standard for Real‑World AI<br>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<br>AI doesn’t fail in benchmarks.<br>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.
What INQUISITOR Gives You<br>A repeatable way to evaluate AI behaviour
A shared vocabulary for describing failures and instabilities
A lightweight workflow for everyday testing
A formal methodology for audit, governance, and reproducibility
A developer‑friendly approach that fits into real tasks, not lab conditions
INQUISITOR is built for people who need AI to behave predictably inside real systems, real teams, and real workflows.
Who INQUISITOR Is For<br>Developers integrating AI into products
Engineers needing predictable behaviour
Analysts working with structured tasks
Researchers validating model behaviour
Product teams assessing reliability
Governance & risk functions needing evidence
Anyone using AI in real workflows
You don’t need expertise.<br>You don’t need special prompts.<br>You don’t need to run every test surface.
You only need to work normally and observe honestly.
What’s Included in This Repository<br>Quick Start Guide
A five‑minute behavioural check. Perfect for fast evaluation.
Practitioner’s Guide
The everyday operational guide. Use this for real‑world testing.
Methodology (GitHub Edition)
The structured, formal framework for reproducible behavioural evaluation.
Licence
Defines usage and redistribution rights for this edition.
How to Use INQUISITOR<br>Run the Quick Start to get a behavioural snapshot.
Use the Practitioner’s Guide for real‑world evaluation.
Apply the Methodology when you need structure, evidence, or audit‑grade documentation.
Follow the Licence for redistribution rules.
INQUISITOR scales from lightweight to formal depending on your needs.
Edition Notes<br>This is the GitHub Edition:
free to share
free to redistribute
free for personal or team use
not permitted for commercial use without permission
Future editions may include:
Enterprise Edition
Full Methodology Edition
Audit & Compliance Edition
Contact<br>For commercial licensing or permissions, contact the author directly.
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Evaluating AI behaviour under real‑world work conditions to surface issues before they become problems. LLM INQUISITOR identifies failures (drift, instability etc) by observing AI during normal tasks — a tool the industry desperately needs to stem the 85% failure rate. Includes Quick Start, Practitioner’s...