AI Evaluation & Safety explicadores.
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Todos los explicadores de AI Evaluation & Safety
Hallucinations: Why LLMs Make Stuff Up Confidently
Hallucinations are not bugs — they are the model doing exactly what it was trained to do. Plausibility is the loss; truth is not. Understand the trap, then engineer around it.
AI Evals: How to Tell If Your Model Is Actually Better
Without evals, "the new prompt feels better" is just vibes. A good eval suite catches regressions before users do — here is how to build one.
Jailbreaks and Guardrails: The Cat-and-Mouse of LLM Safety
Jailbreaks slip past a model's safety training; guardrails sit outside the model and catch what slips. Both are needed; neither is sufficient.
AI Alignment: Making Models Want What We Want
Alignment is the gap between what we say we want and what the model actually optimises. Get it wrong and the model wins by Goodharting your reward.
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