AI / LLM Basics Interview Questions
What is Chain-of-Thought Prompting?
Chain-of-Thought prompting encourages a model to write out its intermediate reasoning steps before giving a final answer, rather than jumping straight to a conclusion.
- Can be triggered explicitly, by asking the model to "think step by step"
- Often noticeably improves accuracy on tasks involving multi-step logic, arithmetic, or reasoning
- Gives a person reviewing the output visibility into how the model arrived at its answer, not just what the answer was
This technique works because it gives the model more computational "room" to work through a problem incrementally, rather than having to produce a correct answer in a single, immediate leap.
What does Chain-of-Thought prompting encourage a model to do?
What kind of tasks does Chain-of-Thought often improve accuracy on?
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