Advanced Prompt Patterns: Tree-of-Thought & Self-Consistency
Master advanced prompt engineering patterns beyond basic prompting — tree-of-thought, self-consistency, generated knowledge, and more. Basic prompt engineering gets you started, but advanced prompting patterns unlock dramatically better performance from AI models. These techniques, discovered through systematic research and community experimentation, can improve reasoning accuracy, reduce hallucination, and enable models to tackle problems they would otherwise fail. Chain-of-Thought Prompting Chain-of-thought (CoT) prompting asks the model to reason step by step before giving a final answer. This simple technique dramatically improves performance on reasoning tasks — math problems, logic puzzles, and multi-step analysis. The key is to explicitly request reasoning before the answer. Instead of "What is 24 37?" ask "Let's work through 24 37 step by step, then give the answer." This forces the model to compute intermediate results rather than guessing the final number. Zero-shot CoT adds "Let's think step by step" to any question. Few-shot CoT provides examples of step-by-step reasoning for similar problems. The technique is most effective for arithmetic, commonsense reasoning, symbolic reasoning, and logical deduction tasks. Tree-of-Thought Prompting Tree-of-thought (ToT) extends chain-of-thought by exploring multiple reasoning paths simultaneously. Instead of following a single chain of reasoning, the model branches out, evaluates different approaches, and selects the most promising path. ToT works by generating multiple "thoughts" at each step, evaluating which thoughts are most promising, pruning unpromising branches, and continuing exploration along promising paths.