self consistency
Applied AI interview questions tagged self consistency, across every topic.
3 questions · 0 unlocked for you
Concepts behind "self consistency"
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
Self-Consistency, Tree-of-Thought, and Prompt ChainingThree ways to push past a single linear chain of thought: self-consistency samples many reasoning paths and votes on the answer, tree-of-thought branches and searches over partial reasoning, and prompt chaining splits one hard prompt into a sequence of focused calls. Each trades extra tokens and latency for accuracy or control. Applied AI interviews probe this to see if you can reach for the right technique instead of reflexively spending 40 samples on every request.🧠 Foundations of LLMs & GenAISign in