# Input Optimization Manage the data your AI applications use for testing and learning. Create manual test inputs and analyze real user data to improve performance. ## Manual Inputs vs End User Inputs ### 📝Manual Inputs Test data you create to optimize and validate your AI application before deployment. #### Purpose: - Provide controlled test scenarios for optimization - Test edge cases and challenging inputs - Establish baseline performance measurements - Guide prompt optimization strategies #### When to use: - Before running any optimization - When discovering new edge cases - To test specific scenarios systematically - For systematic performance validation ### 👥End User Inputs Real data from users interacting with your deployed AI application. #### Purpose: - Monitor real-world performance patterns - Identify new edge cases from actual usage - Track performance trends over time - Optimize based on actual user behavior #### When to use: - After deploying your application - To understand real usage patterns - When performance issues arise in live environments - For continuous improvement strategies ## 3 Input Optimization Tabs Access through Actions → Input Optimization. Each tab serves a different purpose in managing your input data. ### 📋Overview Introduction to Manual vs End User inputs and how to use each type effectively. ### 📝Manual Inputs Create and manage test data for optimization. Add inputs representing different scenarios and edge cases. ### 👥End User Inputs View and analyze real user data with search, filter, and pattern identification capabilities. ## Creating Manual Inputs ### How to Create Manual Inputs 1. **Access Manual Inputs** Click Actions → Input Optimization → Manual Inputs tab 2. **Enter Test Data** Add your test data in the input fields 3. **Include Representative Examples** Add examples that represent real use cases 4. **Save for Optimization** Save the input to use in optimization processes ### Input Structure Each manual input contains: **Input Variables:** The data your application will process **Context:** Additional information or parameters **Expected Scenarios:** What type of situation this represents ## Best Practices for Manual Inputs ### Include Different Input Types - **Positive scenarios:** Inputs that should work well - **Negative scenarios:** Challenging or problematic inputs - **Edge cases:** Unusual or boundary conditions - **Typical usage:** Common, everyday examples ### Focus on Meaningful Examples Each input should test specific scenarios Include inputs that have caused problems before Create inputs based on actual user feedback Regularly update inputs as requirements evolve ### Organize by Scenario Type - **Length variations:** Short, medium, and long inputs - **Content types:** Different subject matters or formats - **Complexity levels:** Simple to complex scenarios - **Language patterns:** Formal, casual, technical language ## Analyzing End User Inputs ### Input Log Features The End User Inputs tab shows real usage data with: #### 📅Timestamp When the API call occurred #### 🔧Input Variables Exact data sent by the user #### 📝Response Generated output from your application #### 📊Score Performance rating for this interaction ### Search and Filter Capabilities - **Search inputs:** Find specific examples or patterns - **Filter by score:** Focus on high or low-performing interactions - **Date ranges:** Analyze performance over time periods - **Pattern identification:** Look for common problem scenarios ## Using Input Data for Optimization ### Before Optimization - Create comprehensive manual inputs covering key scenarios - Run initial optimization to establish baseline performance - Review which inputs perform well vs poorly - Add more inputs for problematic scenarios ### During Optimization - Use manual inputs to test prompt variations - Focus optimization on inputs with low scores - Create new inputs when discovering edge cases - Validate improvements across all input types ### After Deployment - Review End User Inputs regularly for new patterns - Identify consistently low-scoring real user interactions - Add problematic real inputs as manual test cases - Re-optimize based on actual usage patterns ## Input Analysis Strategies ### Identifying Problem Patterns #### Look for: - **Consistent low scores:** Inputs that always perform poorly - **Score variation:** Similar inputs with different performance - **New edge cases:** User inputs you hadn't considered - **Performance degradation:** Scores declining over time #### Common Issues: - **Length-related issues:** Long inputs scoring lower than short ones - **Format problems:** Specific input formats causing confusion - **Content challenges:** Certain topics performing poorly - **User behavior:** Unexpected interaction patterns ### Using Patterns for Optimization 1. Add representative manual inputs for identified problem patterns 2. Create specific evaluations targeting common issues 3. Run targeted prompt optimization for problematic scenarios 4. Test solutions against both manual and real user inputs ## Input Management Workflows ### Initial Setup Workflow - Create foundational manual inputs covering expected use cases - Include edge cases and challenging scenarios - Run initial optimization using manual inputs - Deploy application when manual input performance is satisfactory - Monitor end user inputs for real-world validation ### Ongoing Maintenance Workflow - Weekly end user input review for new patterns - Monthly analysis of score trends and performance changes - Quarterly input refresh adding new manual inputs based on real usage - Continuous optimization when problematic patterns emerge ## Next Steps - [**→ Prompt Optimization**](https://empromptu.ai/optimizer/prompt-optimization) - [**→ Edge Case Detection**](https://empromptu.ai/optimizer/edge-case-detection) - [**→ Evaluations**](https://empromptu.ai/optimizer/evaluations) - [Get Started](https://builder.empromptu.ai/) - [View Documentation](https://empromptu.gitbook.io/empromptu-docs)