An enterprise .NET team experienced significant issues after integrating Azure OpenAI directly into their production application. The primary problems encountered were a lack of observability, leading to difficulties in diagnosing errors and understanding model behavior, and uncontrolled token costs that far exceeded initial estimates. The integration also suffered from high latency, which the existing application architecture could not handle. Solutions involved implementing Semantic Kernel for orchestration and integrating a comprehensive observability pipeline using OpenTelemetry to track prompts, responses, and token usage, which quickly revealed a plugin validation issue as the root cause of incorrect answers. AI
IMPACT Highlights critical challenges in deploying LLMs in production, emphasizing the need for robust observability and cost management for enterprise AI adoption.
RANK_REASON Article describes a common failure pattern for enterprise LLM integrations, focusing on practical implementation challenges rather than a new model or research.
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