Building AI Intensive Python Applications

Rachelle Palmer is the Product Leader for Developer Database Experience and Developer Education at MongoDB, overseeing the driver client libraries, documentation, framework integrations, and MongoDB University. She has built sample applications for MongoDB in Java, PHP, Rust, Python, Node.js, and Ruby. Rachelle joined MongoDB in 2013 and was previously the Director of the Technical Services Engineering team, creating and managing the team that provided support and CloudOps to MongoDB Atlas. Ben Perlmutter is a Senior Engineer on the Education AI team at MongoDB. He applies AI technologies such as LLMs, embedding models, and vector databases to improve MongoDB's educational experience. His team built the MongoDB AI chatbot, which uses RAG to help thousands of users a week learn about MongoDB. Ben formerly worked as a technical writer specializing in developer-focused documentation. Ashwin Gangadhar is a Senior Solutions Architect at MongoDB with over a decade of experience in data-driven solutions for e-commerce, HR analytics, and finance. He holds a master's in Controls and Signal Processing and specializes in search relevancy, computer vision, and NLP. Passionate about continuous learning, Ashwin explores new technologies and innovative solutions. Born and raised in Bengaluru, India, he enjoys traveling, exploring cultures through cuisine, and playing the guitar. Nicholas Larew is a Senior Engineer on MongoDB's Education AI team. He works on MongoDB's AI chatbot, including the open-source framework that powers it, and MongoDB's content generation and dataset curation efforts. Before working in AI, Nicholas wrote and maintained documentation and sample applications for MongoDB's developer-facing products. Sigfrido Narváez is an Executive Solution Architect at MongoDB where he works on AI projects, database migration, and app modernization. His customers span the Americas and LATAM for entertainment, gaming, financial and other verticals. Named a MongoDB Master in 2015, he speaks at conferences such as GDC, QCon, and re:Invent, sharing the sample apps he has built in Python and other languages using MongoDB Atlas and leading AI technologies. Thomas Rueckstiess is a Senior Staff Research Scientist and Head of the Machine Learning Research Group at MongoDB. Thomas holds a PhD in Machine Learning, specializing in neural networks and reinforcement learning, transformers, and structured data modeling. He joined MongoDB in 2012 and was previously the Lead Engineer for MongoDB Compass and Atlas Charts. Henry Weller is the dedicated Product Manager for Atlas Vector Search, focusing on the query features and scalability of the service, as well as developing best practices for users. He helped launch Atlas Vector Search from Public Preview into General Availability in 2023 and continues to lead the delivery of core features for the service. Henry joined MongoDB in 2022 and was previously a data engineer and backend robotics software engineer. Richmond Alake is an AI/ML Developer Advocate at MongoDB, creating technical learning content for developers building AI applications. His background includes ML architecture, optimizing data pipelines, and developing mobile experiences with deep learning. Richmond specializes in GenAI and computer vision, focusing on practical applications and efficient implementations across AI domains. He guides developers on best practices for AI solutions. Shubham Ranjan is a Product Manager at MongoDB for Python and a core contributing member to AI initiatives at MongoDB. He is also a Python developer and has published over 700 technical articles on topics ranging from data science and ML to competitive programming. Since joining MongoDB in 2019, Shubham has held several roles, progressing from a Software Engineer to a Product Manager for multiple products.

• Table of ContentsGetting Started with Generative AIBuilding Blocks of Intelligent ApplicationsLarge Language ModelsEmbedding ModelsVector DatabasesAI/ML Application DesignUseful Frameworks, Libraries, and APIsImplementing Vector Search in AI ApplicationsLLM Output EvaluationRefining the Semantic Data Model to Improve AccuracyCommon Failures of Generative AICorrecting and Optimizing Your Generative AI Application