7 Steps to Building Production GenAI Apps | Ep28

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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.

Connect with Surabhi Bhargava:
LinkedIn:   / surabhibhargava  

Connect with Shane, Sravya, and Hai (let us know YouTube sent you!):
Shane Butler: https://linkedin.openinapp.co/b02fe
Sravya Madipalli: https://linkedin.openinapp.co/9be8c
Hai Guan: https://linkedin.openinapp.co/4qi1r

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.

Chapters:

00:00 Productionizing Machine Learning: The Journey Begins
08:34 Real-World ML: Solving Practical Business Problems
10:19 Building a GenAI Chatbot for Efficient Data Retrieval
18:28 Embeddings and Chunking: Optimizing Retrieval-Augmented Generation
31:23 Mastering Prompt Engineering: Communicating with LLMs
33:38 Crafting Thoughtful Inputs for GenAI
36:06 How to Write Effective AI Prompts
38:26 Evaluating AI Responses: Metrics and Methods
39:46 Human vs. Automated Evaluation Strategies
44:05 Defining Clear Metrics for AI Success
48:07 Using LLMs for Model Evaluation
52:37 AI Personalization: Hyper-Personalized GenAI Experiences
57:06 When to Avoid Using LLMs: Common Mistakes
58:57 Enhancing User Experience with GenAI Feedback Loops
01:02:43 Closing Thoughts & Next Steps in GenAI Development

#ai #generativeai #genai #productionizingai #aiapplications #rag #embeddings #chunking #vectordatabase #llms #promptengineering #aiaccuracy #modeldevelopment #machinelearning #openai #azureai #claudeai #aiprototype #aidevelopment #queryreformulation #evaluationmetrics #personalization #aiworkflow #machinelearningpipeline #dataneighbor