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Core
Self-Consistency, Tree-of-Thought, and Prompt Chaining
Three 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.
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PRACTICE THIS IN REAL QUESTIONS
LLM & GenAI FundamentalsExplain self-consistency and tree-of-thought prompting. When are they worth the extra cost?→LLM & GenAI FundamentalsChain-of-thought isn't improving accuracy on your task. What do you try next?→System Design for AI in ProductionDesign a text-to-SQL feature: let users ask questions in natural language over a real database.→LLM & GenAI FundamentalsWhat is in-context learning, and why does chain-of-thought prompting improve results?→LLM & GenAI FundamentalsWhat is ReAct (Reasoning + Acting) prompting, and why does interleaving thought and action help?→LLM & GenAI FundamentalsWhat is prompt chaining, and why do production systems need output parsers?→
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