# Edge Case Detection Visual analysis tools to identify and resolve problematic inputs that cause low performance. Use scatter plot visualization to find patterns and target specific problem areas for optimization. ## Getting Started ### πŸ“ŠData Requirements Edge Case Detection requires sufficient optimization data to generate meaningful visualizations. **Minimum:** 15-20+ API calls (optimization runs) **Recommended:** 30+ API calls for clear patterns **Best results:** 50+ API calls with diverse inputs ### 🎯Access Method Access Edge Case Detection through the Actions button on any task. Actions β†’ Edge Case Detection Available after running optimizations Updates as you add more data ## Performance Scatter Plot The scatter plot displays each API call as a point on a graph, showing performance patterns and problem areas visually. ### Understanding the Visualization **X-axis:** Represents one performance dimension **Y-axis:** Represents another performance dimension **Each dot:** Represents one input/output interaction **Colors:** Indicate performance score ranges ### Color-Coded Performance Levels #### πŸ”΄ Red Dots (0-3) Significant performance issues requiring immediate attention #### 🟠 Orange Dots (4-6) Moderate performance issues with improvement potential #### πŸ”΅ Blue Dots (7-8) Good performance, may still benefit from fine-tuning #### 🟒 Green Dots (9-10) Excellent performance, can serve as models for optimization ## Common Performance Patterns ### Scattered Performance **Indicates:** Inconsistent performance across different inputs **Solution:** Focus on building more specialized prompts ### Clustered Problems **Indicates:** Systematic issues with certain input types **Solution:** Target specific clusters for optimization ### Clear Separation **Indicates:** Some inputs work well, others consistently fail **Solution:** Create prompt family members for different input types ### Gradual Distribution **Indicates:** General improvement trend **Solution:** Continue current optimization strategy ## Interactive Interface Tools ### Selection Tools **Click and drag:** Select specific areas of the plot **Multiple selections:** Choose several problem areas at once **"Clear selection" button:** Reset any selected areas ### Optimization Actions **"Optimize" button:** Runs targeted optimization on selected inputs **Focused improvement:** Addresses specific problem areas **Efficient targeting:** More effective than general optimization ## Systematic Edge Case Resolution 1. ### Review Overall Distribution Get a sense of general performance patterns across all your data points. 2. ### Identify Red Clusters Find groups of problematic inputs that consistently perform poorly. 3. ### Select Specific Areas Choose dense problem clusters first - these have the highest impact potential. 4. ### Run Targeted Optimization Focus improvement efforts on the selected areas using the "Optimize" button. 5. ### Reassess Results Check if optimization improved the targeted areas and repeat for other problem clusters. ## Problem Prioritization Strategy ### πŸ”΄ High Priority Dense red clusters affecting many inputs Focus on these first for maximum impact Create specific prompt family members Add more manual inputs for these scenarios ### 🟠 Medium Priority Orange clusters with improvement potential Address after resolving red clusters May benefit from targeted evaluations Consider model optimization ### πŸ”΅ Low Priority Isolated red dots representing rare edge cases Address if they represent important scenarios May indicate data quality issues Consider if worth optimizing ### 🟒 Monitor Blue and green areas for performance maintenance Maintain current performance levels Use as examples for successful patterns Monitor for regression ## Integration with Other Tools ### πŸ“Š "+ Input Optimization" **Problem identification workflow:** Use scatter plot to identify problematic input patterns Go to Input Optimization β†’ End User Inputs to find specific examples Add similar examples to Manual Inputs for systematic testing Use these inputs for targeted prompt optimization ### 🎯 "+ Prompt Optimization" **Targeted improvement workflow:** Select problem clusters in scatter plot Run targeted optimization (creates focused events) Review results in Prompt Optimization β†’ Event Log Use Manual Optimization to create specialized prompts ### βœ… "+ Evaluations" **Criteria refinement workflow:** Identify consistent problem patterns in scatter plot Create specific evaluations targeting these problem types Use new evaluations to guide optimization of problem areas Monitor improvement in subsequent scatter plot analysis ## Best Practices ### When to Use Edge Case Detection - **After initial optimization:** Need baseline data to see patterns - **When performance plateaus:** Identify specific areas needing attention - **Regular monitoring:** Weekly or monthly review of performance patterns - **Before major changes:** Understand current problem areas ### Effective Selection Strategies - **Start with obvious clusters:** Target dense red areas first - **Work systematically:** Address one cluster at a time - **Document findings:** Note what types of inputs cause problems - **Test improvements:** Verify that optimization helps selected areas ### Common Mistakes to Avoid - **Optimizing isolated points:** Focus on patterns, not individual failures - **Ignoring successful areas:** Learn from what works well - **Over-optimization:** Don't endlessly optimize "good enough" areas - **Premature analysis:** Wait for sufficient data before drawing conclusions ## Next Steps [**β†’ Input Optimization** Use identified problem patterns to improve your test data](https://empromptu.ai/optimizer/input-optimization) [**β†’ Prompt Optimization** Create targeted prompt family members for problem clusters](https://empromptu.ai/optimizer/prompt-optimization) [**β†’ Evaluations** Develop specific criteria for systematic problem resolution](https://empromptu.ai/optimizer/evaluations) [Get Started](https://builder.empromptu.ai/) [View Documentation](https://empromptu.gitbook.io/empromptu-docs)