
Unlocking Creativity with Azure OpenAI
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In the previous chapter, we covered essential aspects of operationalizing Azure OpenAI (AOAI), focusing on monitoring key metrics such as API call volume, latency, and token usage to optimize performance. We also discussed AOAI resource quotas, highlighting strategies for managing and allocating quotas effectively across resources. Additionally, the chapter introduced the concept of production throughput units (PTUs), a reserved instance crucial for handling production workloads. To build resilient, enterprise-level generative AI applications, we explored scaling AOAI using multiple endpoints along with high availability (HA) and disaster recovery (DR) strategies.
So far, we’ve explored various scenarios where generative AI can streamline workflows and looked at how to optimize models to enhance their performance and reliability. In this chapter, we’ll dive into prompt engineering—a critical skill that allows us to shape the behavior...