ISBN: 9783032267481 - reliable evaluations for llms and ai agents: end-to-end evaluation frameworks for llms and autonomous ai agents (7 resultados)

Paperback. Condición: new. Paperback. This book gives practitioners a concrete, systematic framework for designing evals that make AI systems safe, robust, and customer-ready before they reach production. Drawing on real-world failures, from chatbots that went off the rails to shopping assistants that hallucinated product information, it shows how seemingly small evaluation gaps can cascade into legal, financial, and reputational crisis, and how to close those gaps with disciplined, systematic testing.Moving from foundational concepts to advanced practice, Reliable Evals for LLMs and AI Agents introduces the four core levers of effective evals: sets, templates, metrics, and evaluators. It then extends these to the unique challenges of autonomous AI agents, where systems perceive, reason, act, and adapt in iterative loops that demand fundamentally different eval approaches. Along the way, it guides readers through benchmark selection, custom eval set design, statistical rigor in metrics, human and LLM-as-a-judge rating strategies, and the infrastructure needed to automate evals at scale.For engineering leaders, applied researchers, data scientists, and product teams shipping LLM- and agent-powered experiences, this volume offers a blueprint for building eval flywheels that continuously improve AI quality. It shows how to progress from ad-hoc checks to production-grade eval systems, align model metrics with real user satisfaction, integrate offline evals with online A/B testing, and design accessible interfaces that democratize rigorous testing across an organization. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

Paperback. Condición: Brand New. 203 pages. 6.10x0.46x9.25 inches. In Stock.

Taschenbuch. Condición: Neu. Reliable Evaluations for LLMs and AI Agents | End-to-End Evaluation Frameworks for LLMs and Autonomous AI Agents | Alexei Robsky (u. a.) | Taschenbuch | x | Englisch | 2026 | Springer | EAN 9783032267481 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu. …

Taschenbuch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This book gives practitioners a concrete, systematic framework for designing evals that make AI systems safe, robust, and customer-ready before they reach production. Drawing on real-world failures, from chatbots that went off the rails to shopping assistants that hallucinated product information, it shows how seemingly small evaluation gaps can cascade into legal, financial, and reputational crisis, and how to close those gaps with disciplined, systematic testing.Moving from foundational concepts to advanced practice, Reliable Evals for LLMs and AI Agents introduces the four core levers of effective evals: sets, templates, metrics, and evaluators. It then extends these to the unique challenges of autonomous AI agents, where systems perceive, reason, act, and adapt in iterative loops that demand fundamentally different eval approaches. Along the way, it guides readers through benchmark selection, custom eval set design, statistical rigor in metrics, human and LLM-as-a-judge rating strategies, and the infrastructure needed to automate evals at scale.For engineering leaders, applied researchers, data scientists, and product teams shipping LLM- and agent-powered experiences, this volume offers a blueprint for building eval flywheels that continuously improve AI quality. It shows how to progress from ad-hoc checks to production-grade eval systems, align model metrics with real user satisfaction, integrate offline evals with online A/B testing, and design accessible interfaces that democratize rigorous testing across an organization. 193 pp. Englisch. …

Condición: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt.

Taschenbuch. Condición: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - This book gives practitioners a concrete, systematic framework for designing evals that make AI systems safe, robust, and customer-ready before they reach production. Drawing on real-world failures, from chatbots that went off the rails to shopping assistants that hallucinated product information, it shows how seemingly small evaluation gaps can cascade into legal, financial, and reputational crisis, and how to close those gaps with disciplined, systematic testing.Moving from foundational concepts to advanced practice, Reliable Evals for LLMs and AI Agents introduces the four core levers of effective evals: sets, templates, metrics, and evaluators. It then extends these to the unique challenges of autonomous AI agents, where systems perceive, reason, act, and adapt in iterative loops that demand fundamentally different eval approaches. Along the way, it guides readers through benchmark selection, custom eval set design, statistical rigor in metrics, human and LLM-as-a-judge rating strategies, and the infrastructure needed to automate evals at scale.For engineering leaders, applied researchers, data scientists, and product teams shipping LLM- and agent-powered experiences, this volume offers a blueprint for building eval flywheels that continuously improve AI quality. It shows how to progress from ad-hoc checks to production-grade eval systems, align model metrics with real user satisfaction, integrate offline evals with online A/B testing, and design accessible interfaces that democratize rigorous testing across an organization.…

Taschenbuch. Condición: Neu. This item is printed on demand - Print on Demand Titel. Neuware -This book gives practitioners a concrete, systematic framework for designing evals that make AI systems safe, robust, and customer-ready before they reach production. Drawing on real-world failures, from chatbots that went off the rails to shopping assistants that hallucinated product information, it shows how seemingly small evaluation gaps can cascade into legal, financial, and reputational crisis, and how to close those gaps with disciplined, systematic testing.Moving from foundational concepts to advanced practice, Reliable Evals for LLMs and AI Agents introduces the four core levers of effective evals: sets, templates, metrics, and evaluators. It then extends these to the unique challenges of autonomous AI agents, where systems perceive, reason, act, and adapt in iterative loops that demand fundamentally different eval approaches. Along the way, it guides readers through benchmark selection, custom eval set design, statistical rigor in metrics, human and LLM-as-a-judge rating strategies, and the infrastructure needed to automate evals at scale.For engineering leaders, applied researchers, data scientists, and product teams shipping LLM- and agent-powered experiences, this volume offers a blueprint for building eval flywheels that continuously improve AI quality. It shows how to progress from ad-hoc checks to production-grade eval systems, align model metrics with real user satisfaction, integrate offline evals with online A/B testing, and design accessible interfaces that democratize rigorous testing across an organization.Springer Nature Customer Service Center GmbH, Europaplatz 3,69115 Heidelberg, Germany, 69115 Heidelberg 204 pp. Englisch.…