I asked Claude once to tell me the weather in the capital of France and it confidently told me “Paris, sunny and 72°F” - which sounded great until I realized it had completely made up that weather data. The AI had no idea what the actual weather was, but it gave me a plausible answer anyway.
That’s when I learned about ReAct prompting, which stands for Reasoning and Acting. Instead of jumping straight to an answer and potentially hallucinating details, the AI thinks out loud about what it needs to do, takes actual actions like calling weather APIs or searching databases, observes the real results, and repeats this cycle until it has a verified answer.
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What is ReAct?
ReAct stands for Reasoning plus Acting, and it’s one of the most powerful prompting techniques for solving complex problems that require real-world data.
The model works in an alternating cycle: first it does Reasoning (thinking about what it needs to do next), then it performs an Action (like calling an API, querying a database, or searching the web), then it makes an Observation (examining the results it got back). It repeats this cycle over and over until it has enough information to confidently solve the problem.
And just to be absolutely clear: this has nothing to do with React.js the JavaScript framework. ReAct is all about the iterative cycle of Reasoning → Action → Observation that helps AI agents solve problems methodically.
Example
Let me show you exactly how ReAct works with a concrete example. Let’s say you ask: “What’s the weather in the capital of France?”
Without ReAct, the AI just guesses and might hallucinate: “Paris, sunny and 72°F” - which sounds confident but could be completely wrong.
With ReAct, you get a transparent reasoning process that looks like this:
Thought: I need to find out what the capital of France is first.
Action: Search("capital of France")
Observation: Paris
Thought: Now I need to get the current weather for Paris.
Action: WeatherAPI("Paris")
Observation: 18°C, partly cloudy
Answer: The capital of France is Paris. The current weather there is 18°C and partly cloudy.See the difference? The AI explicitly reasons about what information it needs, takes actions to retrieve that actual data, observes the real results, and only then provides an answer. Every step is transparent and verifiable.
Basic Implementation
If you want to implement ReAct in your own projects, here’s the basic prompt structure you need:
You solve problems using Thought/Action/Observation cycles.
Available actions: Search, Calculate, Lookup, Finish
Format:
Thought: [your reasoning about what to do next]
Action: [the specific action to take]
Observation: [the result you received]
Continue this cycle until you have enough information to Finish with a confident answer.The agent loop works like this: You get the AI’s thought and action from its response, you execute that action in your code, you add the observation result back into the prompt, and you repeat this cycle with a maximum of about 10 steps to prevent infinite loops. Once the AI uses the Finish action, you return the final answer to the user.
ReAct vs Other Approaches
ReAct versus direct answering: When you ask the AI a question directly, it might give you an answer like “$13.13” that could be wrong with no way to verify how it got there. ReAct shows you every calculation step so you can verify the work and trust the result.
ReAct versus Chain of Thought: Chain of Thought is pure internal reasoning where the AI thinks through a problem step by step. ReAct takes this further by actually using external tools and actions to get verifiable information from the real world instead of just reasoning from memory.
ReAct versus tool use without reasoning: If you just give an AI tools without the reasoning component, it doesn’t catch ambiguities that humans would notice. The ReAct reasoning step catches issues like “Paris” potentially meaning Paris, France versus Paris, Texas and asks for clarification or makes intelligent choices based on context.
Use Cases
Research tasks: Imagine you ask “What’s the GDP growth for the latest Olympic gold medal winner in basketball?” The AI searches for the winning country, then searches for that country’s GDP data, and combines both pieces of information into a final answer. Perfect for questions that require multiple lookups.
Debugging problems: You ask “Why am I getting 500 errors on my API?” The AI reads your error logs, checks the problematic code line it finds there, examines the middleware configuration, and identifies that you’re missing the body-parser middleware. It methodically works through the debugging process just like a human developer would.
Data analysis: You need to know “Which product had the highest revenue growth?” The AI queries your Q3 sales data, queries your Q2 sales data for comparison, calculates the growth rates for each product, and compares the results to give you the answer. It does real calculations on real data.
Advanced Patterns
Error recovery: When the initial search fails, a well-designed ReAct agent tries an alternate data source instead of giving up. If the primary API is down, it falls back to a secondary source and gets an approximate answer rather than returning nothing.
Multi-source verification: For controversial topics or critical decisions, the agent checks multiple independent sources, verifies they agree with each other, and only then provides a confident answer. This dramatically reduces the chance of acting on misinformation.
Iterative refinement: When a query is too broad to answer directly, the agent realizes this in the reasoning step, asks for clarification from the user or refines its own search terms, and then gets a specific result that actually answers the question.
Parallel actions: When the agent needs multiple independent data points, it can query all sources simultaneously rather than sequentially, compare the results as they come in, and dramatically reduce the total time to answer.
Best Practices
Define clear actions: Give your agent a well-defined set of actions like Search, Calculate, ReadFile, and Finish. Don’t make it guess what tools are available or how to use them.
Make observations informative: Return detailed observations like “DatabaseError: connection timeout after 5s” instead of just “Error”. The more context the AI has, the better it can reason about what to do next.
Limit iterations: Set a maximum of around 10 iteration steps to prevent infinite loops where the agent gets stuck reasoning in circles without making progress toward an answer.
Validate actions: Check that the AI’s requested actions are valid and safe before executing them. Don’t blindly run whatever the model asks for.
Log the reasoning trace: Keep a complete log of every Thought, Action, and Observation so users can see exactly how you reached the final answer. Transparency builds trust.
Common mistakes to avoid: Don’t create too many overly granular actions that make simple tasks complicated. Don’t let the agent ignore previous observations and repeat the same failed action. Avoid vague thoughts like “Let me search” when you need specific reasoning like “I need population data for Tokyo to answer this question.”
Conclusion
ReAct prompting makes AI agents more reliable because explicit reasoning reduces errors, more transparent because you can see the entire thinking process, more capable because they can use tools and external data, and more flexible because they can handle complex problems that require multiple steps.
The key concepts are simple: Thought (the AI reasons about what to do next), Action (it searches, calculates, or queries something), Observation (it sees the real results), and Iteration (it repeats this cycle until the problem is solved).
You should use ReAct for: Multi-step problems that require breaking down into smaller tasks, situations where you need external data from APIs or databases, complex reasoning that benefits from explicit step-by-step logic, anything involving tool use, and scenarios where you need transparency into how the AI reached its answer.
The future of AI isn’t just about systems that answer questions instantly - it’s about systems that reason carefully about questions and take real actions to find verified answers. That’s what ReAct enables.