Proven, practical techniques for integrating language models into your data science workflows Data Science First: Using Language Models in AI-Enabled Applications, by Intersect AI’s Chief AI Officer John Hawkins, explains how practicing data scientists can integrate language models in data science workflows without abandoning essential principles of reliability, accuracy, and efficacy. Hawkins offers crystal-clear guidance on when, where, and how data scientists can integrate language models into their existing workflows without exposing themselves or their companies to unnecessary risks. This guide walks you through strategic design patterns for incorporating language models into real-world data science projects. It avoids strategies and techniques that rely heavily on proprietary tools that are likely to evolve very quickly (or could disappear entirely) in the near future. Instead, the author presents foundational methodologies that will remain valuable regardless of how individual platforms or services change. The book combines sound theory with practical case studies that cover common data science projects in the education, insurance, telecommunications, media and banking industries. Including customer churn analysis, customer complaint routing and document processing, demonstrating how language models can enhance rather than replace traditional data science methods. You’ll find: Three chapters providing a solid grounding in the ideas, principles and technologies that are used for data science with language modelsNine chapters that discuss specific patterns for integrating language models into data science workflows, including semantic vector analysis, few-shot prompting, retrieval-based applications, synthetic data generation and AI agent developmentReal-world case studies discussing applications like fraud detection, customer churn, translation, document classification and sentiment analysis, with concrete business applicationsComprehensive evaluation methods
