
Yaashi developed cross-repository enhancements for LLM error observability, focusing on the adobe/spacecat-shared and adobe/spacecat-api-service projects. They introduced a standardized LLM_ERROR_PAGES audit type, updating data models and unit tests in JavaScript and YAML to ensure consistent error tracking. By defining and registering a detailed audit results schema, Yaashi enabled unified reporting of LLM failures, including success status and error counts, across both services. Their work in API and backend development established a foundation for operational monitoring, allowing teams to triage LLM-related issues more efficiently. The changes reflect a thoughtful approach to schema definition and configuration management.

August 2025 performance summary: Delivered cross-repo LLM error observability enhancements across adobe/spacecat-shared and adobe/spacecat-api-service, establishing a standardized LLM_ERROR_PAGES audit type, updating data models and tests, and registering the audit schema in the run-audit workflow. These changes improve visibility into LLM failures, enable faster triage, and provide a consistent basis for operational reporting across services.
August 2025 performance summary: Delivered cross-repo LLM error observability enhancements across adobe/spacecat-shared and adobe/spacecat-api-service, establishing a standardized LLM_ERROR_PAGES audit type, updating data models and tests, and registering the audit schema in the run-audit workflow. These changes improve visibility into LLM failures, enable faster triage, and provide a consistent basis for operational reporting across services.
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