ReAct Prompting for Agent Reasoning
What ReAct means
ReAct (Reason + Act) is a prompting pattern where the model explicitly writes out its reasoning ("Thought: I need to find X"), then its action ("Action: search for X"), observes the result, and repeats — making the agent's decision process transparent and debuggable.
Why explicit reasoning improves outcomes
Writing out the "why" before the "what" forces the model to actually reason rather than jumping to a plausible-sounding but wrong action. It also gives you, the builder, a visible trace to debug when something goes wrong.
Implementing a basic ReAct loop
System prompt instructs the model to always respond in Thought/Action/Observation format. Your code parses the Action, executes the real tool, feeds the Observation back, and the loop continues until a Final Answer is produced.
Key Takeaways
- ReAct interleaves explicit reasoning (Thought) with action (Action) and observation.
- Explicit reasoning forces better decisions and gives a debuggable trace.
- A ReAct system prompt structures the model's response format directly.
- Your code parses actions, executes real tools, and feeds results back into the loop.
Write a ReAct system prompt
Write a system prompt that instructs a model to respond in Thought/Action/Observation format for a simple research task, then manually trace 2 loop iterations.