Reasoning Patterns explicadores.
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Todos os explicadores de Reasoning Patterns
Chain-of-Thought Prompting: Get LLMs to Show Their Work
Add "think step by step" and accuracy on multi-step problems jumps. Hide the scratchpad in production. Free quality, almost always.
ReAct Pattern: Reasoning + Acting in AI Agents
ReAct interleaves a Thought, an Action, and an Observation at each step. The "talk to yourself, then do, then look" loop powers most modern agents.
Tree of Thoughts: When LLMs Need to Branch and Backtrack
Tree of Thoughts explores multiple reasoning branches, prunes bad ones, and backtracks. Use it when the right path is not the first one the model picks.
Self-Consistency: Voting Across Multiple LLM Samples
Run the same prompt N times at non-zero temperature, take the majority answer. A few extra calls, big accuracy gains on hard reasoning.
Prompt Chaining: Breaking Complex Tasks Into Steps
Instead of one mega-prompt, chain N small prompts where each step's output feeds the next. Easier to debug, easier to evaluate, easier to evolve.
Reflexion and Self-Critique: AI That Reviews Its Own Work
Reflexion adds a critique-and-revise loop. The model produces output, criticises it, revises. A few cents extra; meaningful quality gain on the right tasks.
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