Removing Span Metric recommendations / Example cleanup (#13275)

* Removing extra guidance language for metrics

* Removing redundant metrics in examples.

* Removing span metrics suggestions from python

* lower casing the docs

* removing incorrectly committed llm files

Author: codyde

docs/platforms/javascript/common/tracing/span-metrics/examples.mdx

@@ -257,11 +257,6 @@ Sentry.startSpan(
       "ui.form_completion_time_ms": 45000,
       "ui.payment_method_changes": 1,
       "ui.address_validation_retries": 0,
-
-      // Client performance
-      "client.page_load_time_ms": 850,
-      "client.payment_widget_load_ms": 650,
-      "client.total_interaction_time_ms": 120000,
     },
   },
   async () => {
@@ -291,7 +286,6 @@ Sentry.startSpan(
       "payment.processing_time_ms": 1200,
 
       // Inventory checks
-      "inventory.check_time_ms": 150,
       "inventory.all_available": true,
 
       // Fulfillment

docs/platforms/javascript/common/tracing/span-metrics/index.mdx

@@ -86,52 +86,6 @@ Sentry.init({
 });
 ```
 
-## Best Practices for Span Metrics
-
-1. **Metric Naming**
-
-   - Use clear, consistent naming patterns
-   - Include the metric category (examples: `db`, `cache`, `http`)
-   - Use snake_case for metric names
-
-2. **Data Types**
-
-   - Use appropriate numeric types for measurements
-   - Use booleans for status flags
-   - Use strings for categorical data
-   - Use arrays when grouping related values
-
-3. **Performance Considerations**
-
-   - Consider the overhead of metric collection
-   - Use sampling when collecting high-frequency metrics
-   - Balance metric granularity with system performance
-
-4. **Debugging and Monitoring**
-   - Include correlation IDs for related operations
-   - Add context that helps with troubleshooting
-
-## Best Practices for Implementation
-
-When implementing span metrics in your application:
-
-1. **Start Small and Iterate**
-
-   - Begin with basic metrics that directly relate to your debugging or performance monitoring needs
-   - Add more detailed tracking as specific debugging needs emerge
-   - Remove metrics that aren't providing actionable insights
-
-2. **Maintain Consistency**
-
-   - Use consistent naming patterns across your application
-   - Document metric meanings and units in your codebase
-   - Share common metrics across similar operations
-
-3. **Focus on Actionability**
-   - Track metrics that help diagnose specific issues
-   - Consider what alerts or dashboard visualizations you'll want to create
-   - Ensure metrics can drive issue resolution or decision making
-
 For detailed examples of how to implement span metrics in common scenarios, see our <PlatformLink to="/tracing/span-metrics/examples/">Span Metrics Examples</PlatformLink> guide.
 
 ## Span Metrics vs. Measurements

docs/platforms/python/tracing/span-metrics/index.mdx

@@ -99,45 +99,4 @@ sentry_sdk.init(
 )
 ```
 
-## Best Practices for Span Metrics
-
-1. **Metric Naming**
-   - Use clear, consistent naming patterns
-   - Include the metric category (examples: `db`, `cache`, `http`)
-   - Use snake_case for metric names
-
-2. **Data Types**
-   - Use appropriate numeric types for measurements
-   - Use booleans for status flags
-   - Use strings for categorical data
-   - Use arrays when grouping related values
-
-3. **Performance Considerations**
-   - Consider the overhead of metric collection
-   - Use sampling when collecting high-frequency metrics
-   - Balance metric granularity with system performance
-
-4. **Debugging and Monitoring**
-   - Include correlation IDs for related operations
-   - Add context that helps with troubleshooting
-
-## Best Practices for Implementation
-
-When implementing span metrics in your application:
-
-1. **Start Small and Iterate**
-   - Begin with basic metrics that directly relate to your debugging or performance monitoring needs
-   - Add more detailed tracking as specific debugging needs emerge
-   - Remove metrics that aren't providing actionable insights
-
-2. **Maintain Consistency**
-   - Use consistent naming patterns across your application
-   - Document metric meanings and units in your codebase
-   - Share common metrics across similar operations
-
-3. **Focus on Actionability**
-   - Track metrics that help diagnose specific issues
-   - Consider what alerts or dashboard visualizations you'll want to create
-   - Ensure metrics can drive issue resolution or decision making
-
 For detailed examples of how to implement span metrics in common scenarios, see our <PlatformLink to="/tracing/span-metrics/examples/">Span Metrics Examples</PlatformLink> guide.