AI Previews - Test Your Products with AI Agents
Learn how to test your products with AI agents, preview AI agent interactions, and optimize your products for conversational commerce discovery.
Using AI Previews To Visualize Your Products In AI Agent Interactions
AI Previews allow you to test how your products appear to AI agents like ChatGPT, Claude, and Gemini. Preview agent interactions, test product discovery, and optimize your catalog for conversational commerce success.
Understanding AI Previews
What Are AI Previews
AI Previews simulate how AI agents discover, interpret, and recommend your products in real-world contexts. They provide an interactive testing environment to help you understand and improve your AI readiness.
Key Features
- Real-Time Testing: Preview actual AI agent interactions
- Conversation Simulation: Test natural conversational flows
- Product Discovery Testing: Evaluate how agents find and rank your products
- Optimization Insights: Receive automated improvement recommendations
Preview Types
Product Discovery Previews
- Test how AI agents locate and interpret your products
- Validate search and recommendation accuracy
- Review visibility and metadata quality
Conversation Previews
- Simulate customer-facing agent conversations
- Analyze question/response accuracy
- Validate conversational experience and tone
Recommendation Previews
- Test recommendation quality and relevance
- Validate product matching accuracy
- Measure response diversity and contextual precision
Setting Up AI Previews
Initial Preview Configuration
Preview Setup Process
- Navigate to Products → AI Previews in your GXO.dev dashboard
- Click Create New Preview
- Choose a preview type (Discovery, Conversation, or Recommendation)
- Configure AI agent and product parameters
AI Agent Selection
- Choose supported agents (ChatGPT, Claude, Gemini, etc.)
- Configure access credentials and preferences
- Set agent parameters such as context length or temperature
Product Selection
- Select products to include in your preview
- Configure test criteria and scenarios
- Define preview frequency or recurrence
Preview Configuration Options
AI Agent Settings
- Select which agents to test
- Configure access and authentication tokens
- Adjust agent parameters for variability testing
- Enable or disable experimental modes
Test Scenarios
- Create product discovery simulations
- Run conversational test flows
- Evaluate recommendation accuracy and diversity
- Measure interaction outcomes
Monitoring and Analytics
- Track preview performance in real time
- Review AI interaction data
- Analyze discovery and recommendation quality
- Receive actionable optimization insights
Testing Product Discovery
Discovery Testing Process
Product Search Testing
- Test how AI agents query your catalog
- Evaluate keyword and metadata mapping
- Analyze discovery ranking and search relevance
- Optimize product data for higher retrieval rates
Product Matching Testing
- Validate contextual matching accuracy
- Test how well AI agents associate related products
- Review matching precision and false positive rates
- Optimize for semantic similarity and contextual fit
Product Information Testing
- Assess completeness of product details
- Check for clarity and structure in descriptions
- Validate image and metadata consistency
- Identify missing or ambiguous product attributes
Discovery Optimization
Search Optimization
- Refine search parameters and metadata fields
- Improve keyword alignment for agent retrieval
- Test relevance across AI models
- Track discovery score improvements over time
Matching Optimization
- Enhance data relationships between products
- Optimize for higher match confidence
- Improve natural language product linking
- Validate improvements through repeated testing
Testing AI Agent Conversations
Conversation Flow Testing
Natural Language Testing
- Simulate real AI-driven product conversations
- Validate flow coherence and responsiveness
- Check for logical structure and clarity
- Evaluate tone, context, and accuracy
Customer Interaction Testing
- Test realistic customer scenarios
- Validate response accuracy and follow-up prompts
- Assess satisfaction and engagement quality
- Optimize agent tone and pacing
Product Recommendation Testing
- Test conversational recommendation accuracy
- Validate response context and product suitability
- Assess completion and conversion outcomes
- Refine product linking and phrasing
Conversation Optimization
Language Optimization
- Enhance readability and engagement
- Improve clarity and contextual understanding
- Test language model adaptation to your products
- Track performance across iterations
Flow Optimization
- Refine conversation flow for natural transitions
- Optimize follow-up suggestions and prompts
- Balance detail and brevity in responses
- Improve satisfaction through conversation mapping
Monitoring and Analytics
Preview Performance Monitoring
Performance Metrics
- AI agent interaction rates
- Product discovery success percentages
- Conversation accuracy and coherence scores
- Recommendation quality and conversion results
Analytics Dashboard
- Real-time metrics visualization
- Historical performance comparisons
- AI agent-level breakdowns
- Automated optimization recommendations
Performance Optimization
Continuous Optimization
- Regularly run preview tests for trending changes
- Compare agent versions and updates
- Track performance deltas and improvements
- Apply recommended optimizations directly
A/B Testing
- Test multiple preview configurations
- Compare discovery and engagement outcomes
- Validate algorithmic improvements
- Deploy the best-performing configurations
Advanced Preview Features
Custom AI Agent Integration
Custom Agent Testing
- Integrate private or in-house AI models
- Validate API performance and discovery behavior
- Test conversation and product context comprehension
- Optimize integrations for your brand voice
API Integration Testing
- Test response latency and reliability
- Validate authentication and payload delivery
- Ensure compliance with ACP standards
- Monitor API-based recommendation performance
Machine Learning Optimization
ML Model Testing
- Evaluate ML model accuracy for discovery
- Validate ranking and classification algorithms
- Analyze false positive and false negative rates
- Continuously retrain and re-evaluate performance
Algorithm Optimization
- Benchmark feed scoring algorithms
- Tune parameters for faster response and precision
- Optimize query and recommendation pipelines
- Validate improvements through regression testing
Troubleshooting AI Previews
Common Issues
Preview Problems
- Failed preview generation or loading
- AI agent connection or timeout errors
- Incorrect or outdated product data
- Slow performance or missing results
AI Agent Issues
- API access or authentication errors
- Model performance degradation
- Inconsistent discovery or recommendation output
- Integration or configuration mismatches
Solutions
Technical Resolution
- Reauthorize AI agent tokens
- Validate network and API connectivity
- Re-sync product data and feeds
- Refresh cache and retry preview
AI Agent Optimization
- Review and refine agent parameters
- Tune temperature and context settings
- Rebalance metadata and descriptions
- Validate improvements with test iterations
Best Practices
Preview Management
Regular Testing
- Conduct periodic AI preview tests
- Compare results across agent types
- Maintain consistent monitoring cycles
- Apply insights from each iteration
Performance Optimization
- Track historical preview results
- Implement data-driven adjustments
- Focus on measurable improvements
- Reassess optimization impact monthly
AI Agent Optimization
Agent Integration
- Maintain valid credentials and tokens
- Monitor latency and response health
- Update integration settings with API changes
- Ensure compliance with AI agent platform guidelines
Conversation Quality
- Focus on clarity, tone, and intent alignment
- Avoid overloading conversations with data
- Keep product descriptions conversational
- Continuously test and refine responses
Next Steps
Now that you understand AI Previews:
Support and Resources
Getting Help
- Documentation: Comprehensive setup and feature guides
- Support: Email support for all subscription plans
- Community: Join our Discord for peer collaboration
- Training: Watch video tutorials and webinars
AI Preview Resources
AI Previews are a vital part of optimizing your product visibility across AI ecosystems. With GXO.dev, you can preview, test, and refine your products for the next generation of agentic commerce.