Working with Unpredictable Geniuses: A Friendly, Hands-On Guide to Testing Language Models

By: Singh, Abhay

Do you use ChatGPT for research? GitHub Copilot for coding? Claude for planning?

Then you've probably wondered: "Wait, is this output actually right?"

Large language models are powerful but unpredictable. They hallucinate facts, flip conclusions on paraphrased inputs, and
confidently fabricate citations. A major airline faced legal action when their chatbot gave wrong policy information.
Lawyers received sanctions for submitting AI-generated fake case citations. Traditional testing doesn't work because
there's no "one right answer."

This book solves that problem with practical, proven testing techniques you can use today-whether you're chatting with AI
for summaries, coding with AI assistants, or building autonomous agents.

WHAT YOU'LL LEARN:

- How to catch hallucinations before they reach production
- Evidence-anchoring: making AI cite its sources (and verify them automatically)
- Property tests that work without ground truth
- Self-consistency checks for high-stakes decisions
- Cost-aware testing strategies that fit your budget
- Statistical methods explained in plain English (no PhD required)
- Complete test harness you can build in a weekend
- Red teaming and adversarial testing for AI security
- Production monitoring and drift detection

THREE READING PATHS IN ONE BOOK:

• Format: Paperback
• Language: English
• ISBN: B0FX2CW361
• ISBN13: 9798270711290
• Release Date: October 2025
• Publisher: Independently Published
• Length: 300 Pages
• Weight: 0.89 lbs.
• Dimensions: 0.6" x 6.0" x 9.0"