Anthropic Chapter 6 Precognition / Thinking Step by Step . The canonical 'let Claude argue both sides before deciding' template. Pairs a savvy reader system prompt with explicit XM
The original Kojima et al. zero shot CoT pattern. Append the trigger to any reasoning question and the model lays out its work instead of jumping to an answer. Source: https://www.
Worked HotpotQA example from Yao et al. 2022 showing the ReAct pattern: interleave reasoning traces with tool actions to ground answers in retrieved evidence. Source: https://githu
Researches both sides of a topic, presents arguments for each, refutes opposing points, and draws conclusions backed by evidence. Source: https://github.com/f/awesome chatgpt promp
Canonical ReAct exemplar from Yao et al. The thought/action/observation rhythm is what makes ReAct prompts work copy this shape when wiring an LLM to a search tool. Source: https:/
From patterns/agents/evaluator optimizer.ipynb . Two cooperating prompts: a generator that produces a solution with <thoughts and <response blocks and an evaluator that returns PAS
Classic Wei et al. few shot CoT exemplars. Each example shows the reasoning trace explicitly, which conditions the model to do the same on the final query. Source: https://www.prom
Wang et al. self consistency uses the same CoT shape as Wei et al., then samples multiple reasoning paths and majority votes. Use these exemplars verbatim and sample at temperature
A prompt chaining pipeline that first pulls verbatim relevant quotes from a long document, then uses those quotes to generate a grounded answer to your question. Use it when you ne
Appendix 10.1 Chaining Prompts . Two turns: 1 ask for ten words ending in 'ab'; 2 follow up with a request to replace any non real words. Demonstrates the multi turn 'have Claude d
Reasoning model system prompt for catching inconsistencies in tabular data. Returns a tight JSON contract that downstream code can branch on. Source: https://cookbook.openai.com/ex
Verbatim system prompt from the Anthropic Research multi agent pattern. The 'research lead' classifies a user query as depth first, breadth first, or straightforward, then plans an
Breaks down math concepts into simple steps with examples and visuals. Source: https://github.com/f/awesome chatgpt prompts
Classic 1 shot example from Brown et al. 2020 GPT 3 paper showing in context learning: define a new word, give one usage example, and the model generalizes. Source: https://github.
Explores philosophical questions, conducts theoretical research, and proposes ethical frameworks. Source: https://github.com/f/awesome chatgpt prompts
Dave Hulbert's plain English distillation of Tree of Thoughts. Works on any chat model no search algorithm required and dramatically improves multi step reasoning. Source: https://
Canonical example from Wei et al. 2022 showing how interleaving worked reasoning into few shot exemplars dramatically improves arithmetic and commonsense tasks. Source: https://git
