chain of thought
AI, ML & GenAI interview questions tagged chain of thought, across every topic.
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Concepts behind "chain of thought"
The curriculum that explains the ideas these questions test.
Foundational
Chain-of-Thought and In-Context LearningIn-context learning is the ability to perform a task from instructions or a few examples in the prompt, with no weight updates. Chain-of-thought prompting has the model reason step by step before answering, which markedly improves multi-step problems (math, logic, multi-hop questions). The catch is that the stated reasoning is not guaranteed to mirror the model's actual computation. AI, ML, and GenAI engineer interviews probe it because it is the cheapest accuracy boost on hard tasks, and because over-trusting the visible reasoning is a real pitfall.🧠 Foundations of LLMs & GenAI
Core
Inference-Time Compute and Reasoning ModelsInference-time (test-time) compute is the idea that spending more computation at generation, longer chains of thought, sampling multiple attempts, or search, reliably improves answers on hard problems, a scaling axis distinct from making the model bigger. Reasoning models (o1/R1-style) are trained, often via RL on verifiable rewards, to produce long internal reasoning and use this. AI, ML, and GenAI interviews probe it because it changed how hard problems get solved and introduced a real latency/cost trade-off: route easy queries to fast models, reserve reasoning models for genuinely hard ones.🧠 Foundations of LLMs & GenAISign in