Chapter 15
How Models Work
Use model mechanics to design better tests: tokenization, context windows, sampling, logits, reward tuning, and multimodal pipelines all create failure modes. Test preference tuning for verbosity, sycophancy, safety, and domain-specific reward mismatch. For image and…
Apply this chapter
- Use model mechanics to design better tests: tokenization, context windows, sampling, logits, reward tuning, and multimodal pipelines all create failure modes.
- Test preference tuning for verbosity, sycophancy, safety, and domain-specific reward mismatch.
- For image and vision-language models, test inputs, extracted evidence, final answers, and safety filters together.
- Expect fine-tuning and model updates to regress capabilities outside the target task.
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Concepts in this chapter
- 058Voice and Multimodal AI TestingVoice and multimodal systems add new failure modes before the model even starts reasoning.
- 113How Modern LLMs Are Trained and TestedTo test LLMs well, builders need a practical model of how they are made.
- 114Testing LLM Training Data and AI PollutionThe model learns from the data it eats, including bad data, stale data, biased data, and increasingly AI-generated data.
- 115Testing RLHF, RLAIF, and Reward Model BehaviorPreference tuning teaches models what gets rewarded. That is not the same as teaching truth.
- 116Useful and Useless LLM Bug ReportsA single bad answer is a clue. It is rarely a complete LLM bug report.
- 117Visualizing, Debugging, and Editing LLM ConceptsModern interpretability tools can reveal useful clues inside models, but they are instruments, not magic explanations.
- 118How Modern LLMs Work: A Block DiagramA simple architecture map helps Confidence Engineers know where failures can enter the system.
- 119Mechanism-Aware LLM Testing: The Strawberry TrapThe famous "how many r's are in strawberry?" question is a useful lesson, but a poor standalone test.
- 120How Image Generation Models WorkImage generation is usually a denoising process guided by text, seed, model, and safety constraints.
- 121How Vision-Language Models Process ImagesVision-language models do not see like people. They encode images into tokens and reason over imperfect visual representations.
- 122Fine-Tuned Models and Regression RiskA fine-tune can improve one behavior while quietly damaging another. Validate the whole model, not only the task you tuned for.