← 🧠 Foundations of LLMs & GenAI
Core
Chain-of-Thought Prompting
Chain-of-thought prompting tells a model to write out intermediate reasoning steps before its final answer, which raises accuracy on multi-step math, logic, and planning by spending more compute per problem. FDE loops probe it because knowing when it helps, when it just burns tokens, and why the stated reasoning is not always the true cause of the answer separates people who have shipped from people who have read a blog post.
a free account unlocks the core curriculum tier · no card
RELATED CONCEPTS
PRACTICE THIS IN REAL QUESTIONS
LLM & GenAI FundamentalsA customer wants the model to 'know our docs.' Prompting, RAG, or fine-tuning: how do you choose?→LLM & GenAI FundamentalsWhen does fine-tuning beat few-shot prompting? How much data do you need, and how do you prevent regressions?→LLM & GenAI FundamentalsSpend a 1000-token test-time budget on a math problem: process-reward scoring with tree search.→LLM & GenAI FundamentalsHere are 30 examples where our prompt gets the wrong answer. Improve it, show me the eval before and after, and do not overfit to the 30.→