ReAct Prompting
Sometimes an AI needs more than just reasoning – it needs to act. ReAct (Reason + Act) prompting combines step‑by‑step reasoning with the ability to call tools or take actions (like searching the web, calculating, or querying a database). The model thinks, then acts, then thinks again based on the result.
ReAct = Thought → Action → Observation → Thought → Action → ...
How ReAct Works
The model alternates between three types of outputs:
- Thought: What should I do next? (Reasoning)
- Action: What tool to use and with what input?
- Observation: The result of the action (provided by the system).
Example: Weather Query
Thought: I need to know the current weather in London.
Action: search[weather in London today]
Observation: 18°C, cloudy.
Thought: The user asked for temperature, so I can answer now.
Final Answer: The current temperature in London is 18°C and cloudy.Why ReAct Is Powerful
- Overcomes knowledge cutoff by fetching live data.
- Can perform calculations or use APIs.
- Reduces hallucinations because actions ground the model in real information.
Implementation Notes
ReAct is typically implemented in frameworks like LangChain, not by hand‑typing a single prompt. However, you can simulate simple ReAct with carefully crafted prompts that instruct the model to output structured actions.
Two Minute Drill
- ReAct = Reason + Act (thought, action, observation loop).
- Enables tool use (web search, calculator, database).
- Reduces hallucinations by fetching real information.
- Commonly used with LangChain or similar frameworks.
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