Building LLM-Based Applications with Azure OpenAI with Jay Emery - #657
Nov 28, 2023
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In a captivating discussion, Jay Emery, Director of Technical Sales & Architecture at Microsoft Azure, shares insights on crafting applications using large language models. He tackles challenges organizations face, such as data privacy and performance optimization. Jay reveals innovative techniques like prompt tuning and retrieval-augmented generation to enhance LLM outputs. He also discusses unique business use cases and effective methods to manage costs while improving functionality. This conversation is packed with practical strategies for anyone interested in the AI landscape.
Prompt engineering and retrieval augmented generation (RAG) are effective techniques for enhancing language model responses.
Choosing the right model, utilizing parallelization strategies, and managing token and cost usage are crucial for successful implementation of language models in business systems.
Deep dives
Leveraging LLMs in Startups and Digital Natives
Startups and digital natives are increasingly leveraging large language models (LLMs) to drive business impact. By utilizing prompt engineering, companies can enhance their prompts to get more robust and specific responses from LLMs. Additionally, fine-tuning LLMs is an option that allows customization but can be expensive and time-consuming. Another approach is the use of retrieval augmented generation (RAG), which retrieves information from an external corpus to generate rich and specific responses. Startups are also focusing on cost management by using the right models, pre-processing to determine the best model for each request, and optimizing token usage. Performance management is addressed by leveraging API rate limits, committed tokens, and pre-processing for choosing the right LLM model. The future of LLMs is expected to bring improvements in performance, energy efficiency, and multimodal capabilities, such as incorporating pictures, video, and 3D models.
Challenges and Considerations in LLM Implementation
Implementing LLMs in core business systems and workflows can pose challenges related to performance, latency, and cost. Startups often face the need to fine-tune LLMs for their specific use cases, but this can be expensive and resource-intensive. Missed conceptions around data privacy and security can also arise. To address these challenges, organizations should consider choosing the right LLM model, utilizing parallelization strategies, and leveraging provision throughput units (PTUs) for guaranteed performance. Cost management and token management are crucial aspects, with startups being advised to start with tried and true models, optimize usage, and explore options like prompting and rag before undertaking fine-tuning. The integration of LLMs into core business systems requires careful architecture and considerations for performance, latency, and costs.
Use Cases and Innovations in LLM Applications
Startups and digital natives are increasingly using LLMs for various applications. Prompt engineering enables startups to generate customer sales collateral, create educational materials, and improve sales pitches. Fine-tuning LLMs assists in generating domain-specific language for workflow processes and creating custom sales collateral. Retrieval augmented generation (RAG) plays a significant role in creating customer sales collateral and coaching sales teams through analyzing transcripts. Use cases also include using LLMs for diversity and inclusion analysis, language coaching, and content moderation. By leveraging LLMs and integrating them into their products and services, startups are revolutionizing industries like education, sales, and content generation.
Predictions and Future Developments in LLMs
The future of LLMs holds promise for enhanced performance and capabilities. Models with improved performance and energy efficiency are expected to emerge. Multimodal capabilities, involving input of images and 3D models, may become a reality. Announcement of the GPT Store and other upcoming platforms indicates a strong commitment from the OpenAI team to connect with the developer community and enable their success. Future developments may include leveraging quantum computing for further advancements in LLMs as well as potential innovations announced at major conferences like Ignite.
Today we’re joined by Jay Emery, director of technical sales & architecture at Microsoft Azure. In our conversation with Jay, we discuss the challenges faced by organizations when building LLM-based applications, and we explore some of the techniques they are using to overcome them. We dive into the concerns around security, data privacy, cost management, and performance as well as the ability and effectiveness of prompting to achieve the desired results versus fine-tuning, and when each approach should be applied. We cover methods such as prompt tuning and prompt chaining, prompt variance, fine-tuning, and RAG to enhance LLM output along with ways to speed up inference performance such as choosing the right model, parallelization, and provisioned throughput units (PTUs). In addition to that, 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.
The complete show notes for this episode can be found at twimlai.com/go/657.
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