chain of thought
Applied AI 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 asks the model to 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 reflect the model's actual computation. Applied-AI 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 exploit this. Applied-AI interviews probe it because it changed how hard problems are 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