ReAct (Reasoning and Acting) Prompting
Learn how to combine reasoning and acting in a thought-action loop to solve complex tasks using external tools
What is ReAct Prompting?
ReAct (Reasoning and Acting) prompting is a paradigm that enables AI models to solve complex tasks by combining natural language reasoning with external tool interactions. It mimics human problem-solving by creating a thought-action loop where the model reasons about the problem, takes actions to gather information, observes results, and iteratively refines its approach until reaching a solution.
Why Use ReAct Prompting?
- Complex Problem Solving: Handles multi-step tasks requiring external information
- Dynamic Information Access: Retrieves real-time data through tool interactions
- Human-like Reasoning: Mirrors how humans think and act to solve problems
- Iterative Improvement: Learns from action results to refine strategies
- Tool Integration: Seamlessly combines reasoning with external capabilities
- Transparent Process: Shows the thinking and action steps for explainability
- Agent-like Behavior: First step towards autonomous agent modeling
How ReAct Prompting Works
ReAct operates through a continuous thought-action loop:
- Thought: The model reasons about the current state and plans next actions
- Action: The model executes tools or queries to gather information
- Observation: The model processes the results from actions
- Iteration: The cycle repeats with updated understanding until goal completion
This process requires careful prompt management, including maintaining conversation history and trimming excessive content to stay within context limits.
Basic Implementation in Latitude
Here’s a simple ReAct example using Latitude’s built-in tools:
Advanced ReAct Implementation
For more complex tasks, create structured ReAct workflows:
Domain-Specific ReAct Applications
Market Research ReAct
Technical Problem Solving ReAct
Content Creation ReAct
Best Practices for ReAct Prompting
Advanced ReAct Techniques
Multi-Agent ReAct
Coordinate multiple specialized agents in ReAct loops:
Hierarchical ReAct
Structure ReAct with multiple levels of planning:
Self-Correcting ReAct
Implement error detection and correction in ReAct loops:
Integration with Other Techniques
ReAct prompting combines effectively with other approaches:
- Chain-of-Thought + ReAct: Detailed reasoning within each thought phase
- Self-Consistency + ReAct: Multiple ReAct cycles to verify solutions
- Step-Back + ReAct: Establish principles before action planning
- Few-Shot + ReAct: Provide examples of effective thought-action patterns
Common Patterns and Templates
The “Investigation” Pattern
- Thought: Identify information gaps
- Action: Gather specific data
- Observation: Analyze findings
- Repeat: Drill deeper or pivot based on results
The “Problem-Solving” Pattern
- Thought: Hypothesize solutions
- Action: Test hypotheses with tools
- Observation: Evaluate effectiveness
- Iterate: Refine approach based on results
The “Synthesis” Pattern
- Thought: Plan comprehensive analysis
- Action: Gather diverse information sources
- Observation: Compare and contrast findings
- Conclude: Synthesize insights into coherent solution
ReAct prompting transforms AI from passive responders to active problem-solvers, enabling complex task completion through iterative reasoning and tool-assisted action cycles.