Generative AI applications are transforming industries, but taking a GenAI model from prototype to production can be challenging. How can teams effectively build, evaluate, and deploy powerful generative AI systems in real-world scenarios? In this episode of the Data Neighbor Podcast, we're joined by Surabhi Bhargava, a Machine Learning Tech Lead at Adobe, to explore the step-by-step process of creating and productionizing GenAI apps, including embedding strategies, chunking techniques, retrieval-augmented generation (RAG), prompt engineering, and advanced model evaluation.In this episode, you'll learn essential insights into how to build a GenAI app, including how to select the right embeddings and chunk size, effective vector database management, and methods for robust query reformulation. Discover best practices for integrating GPT, Claude, Azure AI, and OpenAI APIs into your machine learning pipelines. Surabhi also shares critical tips on optimizing your AI prototype for user testing, identifying the ideal tech stack, and managing iterative feedback.We explore how to properly evaluate GenAI models using automated and human-in-the-loop strategies, discuss practical metrics to measure AI accuracy and performance, and reveal common pitfalls in AI application development. You'll also gain insights into personalization, user experience considerations, resource management, and understanding when not to use LLMs.If you’re a data scientist, engineer, product manager, or executive looking to deepen your understanding of generative AI and effectively move AI projects from concept to production, this episode is your ultimate guide.

Connect with Surabhi Bhargava:LinkedIn: https://www.linkedin.com/in/surabhibhargava/Connect with Shane, Sravya, and Hai (let us know which platform sent you!):Shane Butler: https://linkedin.openinapp.co/b02feSravya Madipalli: https://linkedin.openinapp.co/9be8cHai Guan: https://linkedin.openinapp.co/4qi1r