Hallucination at scale, response quality, safety & toxicity, multi-turn eval
4 sections
Move beyond sample-based checking to production-scale hallucination detection. Learn detection strategies, statistical sampling, sentinel queries, and how to catch model drift before users do.
Learn to evaluate coherence, completeness, tone, and format adherence — the dimensions that determine whether an LLM response is actually usable, not just technically correct.
Build a systematic safety eval pipeline — red-teaming, toxicity detection, jailbreak testing, prompt injection defense, and production content monitoring.
Learn to evaluate chatbot quality across an entire conversation — context tracking, consistency, session-level goal completion, and how to detect the 'memory cliff' where bots forget what users told them.