Building LLM-Based Applications with Azure OpenAI with Jay Emery - #657
Analysis
This article from Practical AI discusses the challenges and solutions for building LLM-based applications using Azure OpenAI. It features an interview with Jay Emery from Microsoft Azure, covering crucial aspects like security, data privacy, cost management, and performance. The discussion explores prompting techniques, fine-tuning, and Retrieval-Augmented Generation (RAG) for enhancing LLM output. Furthermore, it touches upon methods to improve inference speed and showcases real-world use cases leveraging Azure Machine Learning prompt flow and AI Studio. The article provides a comprehensive overview of practical considerations for businesses adopting LLMs.
Key Takeaways
- •The article highlights the importance of addressing security, data privacy, cost, and performance when building LLM applications.
- •It explores various techniques for improving LLM output, including prompt tuning, fine-tuning, and RAG.
- •The discussion covers methods to optimize inference speed, such as model selection and parallelization.
“Jay also shared several intriguing use cases describing how businesses use tools like Azure Machine Learning prompt flow and Azure ML AI Studio to tailor LLMs to their unique needs and processes.”