Chain-of-Thought Reasoning
Chain-of-thought (CoT) reasoning is a prompting technique that instructs an AI model to articulate its intermediate reasoning steps before producing a final answer, significantly improving accuracy on complex multi-step problems.
理解する Chain-of-Thought Reasoning
Discovered through research at Google Brain, chain-of-thought prompting involves adding 'Let's think step by step' or showing examples with explicit reasoning chains to LLM prompts. This simple change dramatically improves performance on arithmetic, logical reasoning, and planning tasks by giving the model space to work through the problem incrementally rather than jumping directly to an answer. The underlying mechanism is that generating intermediate steps constrains the model's output distribution toward logically coherent reasoning paths. Mistakes in early steps can be caught before they propagate, and the model's computation is distributed across more tokens. Chain-of-thought is especially important for AI agents. Before deciding which tool to call or what action to take, an agent benefits from reasoning through the situation: what does the user want, what information do I have, what tools are available, and what is the most logical sequence of steps? This explicit reasoning phase makes agent behavior more predictable and easier to debug. Variants include zero-shot CoT (adding 'think step by step' to any prompt), few-shot CoT (providing examples with reasoning chains), and tree-of-thought (exploring multiple reasoning branches and selecting the best). Modern models like Claude and GPT-4o have CoT capabilities built into their training.
GAIAの活用方法 Chain-of-Thought Reasoning
GAIA's agent prompts encourage chain-of-thought reasoning before taking actions. When processing a complex email or planning a multi-step workflow, the LLM first reasons through the situation: what is the intent, what context is available, which tools are needed, and in what order. This reasoning phase reduces errors in tool selection and workflow planning, making GAIA's autonomous actions more reliable and auditable.
関連概念
Prompt Engineering
Prompt engineering is the practice of designing and refining inputs to AI language models to reliably elicit desired outputs, shaping model behavior without modifying the underlying weights.
Few-Shot Learning
Few-shot learning is the ability of an AI model to adapt to a new task or output format from just a small number of input-output examples provided in the prompt, without any weight updates.
AIエージェント
AIエージェントとは、環境を認識し、状況に応じた判断を下し、特定の目標を継続的な人間の指示なしに達成するために自律的に行動するソフトウェアシステムです。
AIオーケストレーション
AIオーケストレーションとは、単独では処理できない複雑なマルチステップタスクを完了するために、複数のAIエージェント、モデル、およびツールを連携させることです。
大規模言語モデル（LLM）
大規模言語モデル（LLM）は、膨大なテキストデータでトレーニングされた人工知能モデルであり、人間のような流暢さで言語を理解、生成、推論できます。
