← 🧠 Foundations of LLMs & GenAI
Core
Reward Models
A reward model scores a candidate output by how much a human would prefer it, learned from pairwise comparisons rather than absolute ratings. It is the signal that drives RLHF, ranks best-of-N samples, and guides test-time search, which is why FDE loops probe how it is trained, where it leaks, and the outcome-versus-process distinction.
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RELATED CONCEPTS
PRACTICE THIS IN REAL QUESTIONS
LLM & GenAI FundamentalsWhat's the difference between a base model and an instruct model, and why would a customer care?→Machine Learning & Data ScienceA customer says 'we want the model to be accurate.' How do you turn that into the metric you'll optimize?→AI Security, Privacy & GovernanceReproduce-from-paper: design a production-safe Constitutional-AI-style fine-tuning pipeline that aligns a chatbot to a set of rules→LLM & GenAI FundamentalsWalk through the RLHF pipeline (SFT, reward model, PPO) and explain how DPO simplifies it.→LLM & GenAI FundamentalsInstruction tuning: win-rate keeps rising but factual QA accuracy drops. What's happening and the immediate fix?→Behavioral & Customer ScenariosYour AE promised the customer a capability that doesn't exist, the contract is signed, and the customer is holding you to it. Handle it.→