​
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:
  1. Thought: The model reasons about the current state and plans next actions
  2. Action: The model executes tools or queries to gather information
  3. Observation: The model processes the results from actions
  4. 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: 
---
provider: OpenAI
model: gpt-4o
temperature: 0.1
tools:
  - latitude/search
  - latitude/extract
---

# Research Assistant with ReAct

I will help you research {{ research_topic }} using a systematic thought-action approach.

## Instructions:
Follow this ReAct pattern:
1. **Thought**: Reason about what information you need
2. **Action**: Use tools to gather that information
3. **Observation**: Analyze the results
4. **Thought**: Plan your next step based on what you learned
5. Repeat until you have comprehensive information

Let me start researching {{ research_topic }}:

**Thought**: I need to understand the current state and recent developments in {{ research_topic }}. Let me start with a broad search to get an overview.

**Action**: I'll search for recent information about {{ research_topic }}.

​
Advanced ReAct Implementation

For more complex tasks, create structured ReAct workflows: 
---
provider: OpenAI
model: gpt-4o
temperature: 0.2
tools:
  - latitude/search
  - latitude/extract
  - latitude/code
---

<step>
# ReAct Problem Solving Framework

I'm tasked with: {{ complex_task }}

## Initial Assessment:
**Thought**: Let me break down this complex task and identify what information and actions I need to complete it successfully.

1. **Problem Analysis**: What are the key components of this task?
2. **Information Requirements**: What data do I need to gather?
3. **Tool Strategy**: Which tools will be most effective?
4. **Success Criteria**: How will I know when the task is complete?

Let me begin the ReAct process:
</step>

<step>
# ReAct Execution Cycle

Previous context: {{ initial_assessment }}

## Thought-Action Loop:

**Thought 1**: Based on my analysis, I need to start by {{ first_reasoning_step }}

**Action 1**: [Execute first action using appropriate tools]

**Observation 1**: [Process and analyze results]

**Thought 2**: Given these results, my next step should be {{ next_reasoning_step }}

**Action 2**: [Execute second action]

**Observation 2**: [Analyze new information]

Continue this pattern until task completion...
</step>

<step>
# Synthesis and Conclusion

Previous ReAct cycles: {{ execution_cycles }}

## Final Synthesis:
**Thought**: Now I need to synthesize all the information I've gathered and provide a comprehensive response.

**Final Analysis**: [Combine all observations and reasoning]

**Conclusion**: [Present final results and recommendations]

**Reflection**: [Evaluate the effectiveness of the ReAct process]
</step>

​
Domain-Specific ReAct Applications

​
Market Research ReAct

---
provider: OpenAI
model: gpt-4o
temperature: 0.3
tools:
  - latitude/search
  - latitude/extract
  - latitude/code
---

# Market Research Agent

Research market opportunity for: {{ product_concept }}

## ReAct Research Process:

**Thought**: To assess this market opportunity, I need to gather data on market size, competition, trends, and customer needs. Let me start systematically.

**Action**: Search for market size and growth data for {{ product_concept }}

[Tool will execute search]

**Observation**: [Analyze market size data]

**Thought**: Now I need competitive intelligence. Who are the key players and what gaps exist?

**Action**: Search for competitors and competitive analysis in {{ market_segment }}

[Continue ReAct cycle through:]
- Market trends analysis
- Customer pain points research
- Regulatory considerations
- Technology landscape assessment

**Final Market Assessment**: [Synthesized conclusion]

​
Technical Problem Solving ReAct

---
provider: OpenAI
model: gpt-4o
temperature: 0.1
tools:
  - latitude/search
  - latitude/code
  - latitude/extract
---

# Technical Debugging Assistant

Debug and solve: {{ technical_problem }}

## ReAct Debugging Process:

**Thought**: I need to understand this technical issue systematically. Let me gather information about the error, check documentation, and test potential solutions.

**Action**: Search for common causes and solutions for {{ error_type }}

**Observation**: [Analyze search results for patterns]

**Thought**: Based on these patterns, let me examine the specific technical details and run some diagnostic code.

**Action**: Execute diagnostic code to analyze {{ system_component }}

**Observation**: [Review diagnostic results]

**Thought**: The diagnostics suggest {{ hypothesis }}. Let me verify this with additional research and testing.

[Continue ReAct cycle through:]
- Documentation research
- Code analysis
- Solution testing
- Validation steps

**Solution**: [Present debugged solution with reasoning]

​
Content Creation ReAct

---
provider: OpenAI
model: gpt-4o
temperature: 0.4
tools:
  - latitude/search
  - latitude/extract
---

# Strategic Content Creator

Create content strategy for: {{ content_topic }}

## ReAct Content Development:

**Thought**: To create effective content, I need to understand the audience, analyze successful content in this space, and identify unique angles.

**Action**: Research trending content and successful approaches for {{ content_topic }}

**Observation**: [Analyze content trends and engagement patterns]

**Thought**: Now I need to understand the target audience better and identify content gaps.

**Action**: Search for audience demographics and content preferences in {{ target_market }}

**Observation**: [Review audience insights]

**Thought**: With this audience data, let me identify unique angles and content opportunities.

[Continue ReAct cycle through:]
- Competitive content analysis
- SEO and keyword research
- Platform-specific optimization
- Content format testing

**Content Strategy**: [Present comprehensive strategy]

​
Best Practices for ReAct Prompting

Clear Pattern Establishment:
  • Always label thoughts, actions, and observations explicitly
  • Maintain consistent format throughout the conversation
  • Ensure each thought logically leads to the next action
  • Make observations comprehensive and actionable
Reasoning Quality:
  • Encourage detailed reasoning in thought phases
  • Connect new information to previous observations
  • Show how each action builds toward the goal
  • Maintain logical flow between iterations
Effective Tool Usage:
  • Choose appropriate tools for each information need
  • Combine multiple tools when necessary
  • Use tool results to inform subsequent actions
  • Validate information across multiple sources
Tool Strategy:
  • Start with broad searches, then narrow focus
  • Use extraction tools for detailed analysis
  • Employ code tools for calculations and data processing
  • Chain tool calls for complex workflows
Conversation History:
  • Maintain relevant context from previous cycles
  • Trim excessive detail while preserving key insights
  • Reference previous observations in new reasoning
  • Build cumulative understanding over iterations
Memory Optimization:
  • Summarize key findings periodically
  • Remove redundant information to save tokens
  • Prioritize recent and relevant context
  • Use step-based approaches for complex tasks
Validation Techniques:
  • Cross-verify information from multiple sources
  • Test hypotheses through targeted actions
  • Evaluate solution effectiveness before concluding
  • Maintain skeptical reasoning throughout
Error Handling:
  • Acknowledge when tools return unexpected results
  • Adjust strategy based on failed actions
  • Seek alternative information sources
  • Document limitations and assumptions

​
Advanced ReAct Techniques

​
Multi-Agent ReAct

Coordinate multiple specialized agents in ReAct loops: 
---
provider: OpenAI
model: gpt-4o
temperature: 0.3
tools:
  - latitude/search
  - latitude/extract
  - latitude/code
type: agent
agents:
  - agents/researcher
  - agents/analyst
  - agents/strategist
---

# Multi-Agent ReAct Coordination

Task: {{ complex_multi_faceted_task }}

## Coordinated ReAct Process:

**Coordination Thought**: This task requires multiple specialized perspectives. Let me coordinate researcher, analyst, and strategist agents in a ReAct workflow.

**Action**: Initiate research phase with specialist agents

[Agents execute their ReAct cycles]

**Observation**: Synthesize findings from all agent perspectives

**Coordination Thought**: Based on multi-agent insights, determine next coordinated actions

[Continue coordinated ReAct cycles]

**Final Synthesis**: Integrate all agent findings into comprehensive solution

​
Hierarchical ReAct

Structure ReAct with multiple levels of planning: 
---
provider: OpenAI
model: gpt-4o
temperature: 0.2
tools:
  - latitude/search
  - latitude/extract
  - latitude/code
---

<step as="strategic_plan">
# Strategic ReAct Planning

High-level task: {{ strategic_objective }}

**Strategic Thought**: I need to break this into manageable sub-goals and plan a hierarchical approach.

## Strategic Planning:
1. **Goal Decomposition**: Break into sub-objectives
2. **Priority Setting**: Determine order of operations
3. **Resource Planning**: Identify tool and information needs
4. **Success Metrics**: Define completion criteria

**Strategic Action**: Define detailed execution plan
</step>

<step as="tactical_results">
# Tactical ReAct Execution

Strategic plan: {{ strategic_plan }}

## Tactical ReAct Cycles:

For each sub-objective:
**Tactical Thought**: [Specific reasoning for this sub-goal]
**Tactical Action**: [Focused tool usage]
**Tactical Observation**: [Sub-goal specific analysis]

[Repeat for each tactical objective]
</step>

<step>
# Operational ReAct Implementation

Tactical progress: {{ tactical_results }}

## Operational Actions:

**Operational Thought**: Now execute specific implementation steps
**Operational Action**: [Detailed implementation]
**Operational Observation**: [Immediate results]

[Rapid operational cycles]

**Integration**: Combine operational results with tactical and strategic levels
</step>

​
Self-Correcting ReAct

Implement error detection and correction in ReAct loops: 
---
provider: OpenAI
model: gpt-4o
temperature: 0.3
tools:
  - latitude/search
  - latitude/extract
  - latitude/code
---

# Self-Correcting ReAct System

## Enhanced ReAct with Error Detection:

**Thought**: [Standard reasoning]
**Action**: [Tool execution]
**Observation**: [Standard result analysis]

**Validation Thought**: Let me check if this information seems accurate and complete
**Validation Action**: Cross-reference with additional sources
**Validation Observation**: [Quality assessment]

**Correction Thought**: [If errors detected] I need to correct my approach because [reasoning]
**Correction Action**: [Alternative approach]
**Correction Observation**: [Verified results]

**Meta-Thought**: Evaluate the effectiveness of my ReAct process and adjust if needed

[Continue with improved approach]

​
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.