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

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.