OpenAI Development Guide (eBook, ePUB)
Definitive Reference for Developers and Engineers
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"OpenAI Development Guide" The "OpenAI Development Guide" is a comprehensive, state-of-the-art resource for engineers, architects, researchers, and technology leaders seeking to master development with OpenAI's pioneering artificial intelligence tools. It begins with a thoughtful exploration of OpenAI's history and philosophy, providing essential insights into the evolution, architecture, and taxonomy of its large language models. Readers are introduced to foundational concepts such as transformer architectures, training strategies, and the diverse families of OpenAI models-including GPT, DALL...
"OpenAI Development Guide"
The "OpenAI Development Guide" is a comprehensive, state-of-the-art resource for engineers, architects, researchers, and technology leaders seeking to master development with OpenAI's pioneering artificial intelligence tools. It begins with a thoughtful exploration of OpenAI's history and philosophy, providing essential insights into the evolution, architecture, and taxonomy of its large language models. Readers are introduced to foundational concepts such as transformer architectures, training strategies, and the diverse families of OpenAI models-including GPT, DALL-E, and Whisper-setting a robust groundwork for effective, responsible AI development.
Moving from foundational knowledge to practical integration, the guide offers in-depth coverage of OpenAI API programming, advanced integration scenarios, and innovative prompt engineering techniques. It discusses best practices around secure authentication, optimization of API workflows, robust error handling, and cutting-edge methods for harnessing multi-modal capabilities. Detailed chapters on custom model training, fine-tuning workflows, and deployment strategies empower developers to build, evaluate, and optimize domain-specific AI solutions, while maintaining rigorous security, privacy, and ethical standards throughout.
The latter sections focus on real-world considerations and advanced architectural patterns, presenting proven approaches for scaling AI applications, managing operational complexity, and maintaining high performance. Readers will find expert guidance on DevOps, monitoring, observability, and chaos engineering tailored to AI workloads. Case studies and future-facing discussions showcase the transformative power of OpenAI in applications such as conversational agents, document understanding, compliance automation, and knowledge graph enhancement, grounding the technology in both today's best practices and tomorrow's opportunities. This guide is an essential companion for anyone striving to stay at the forefront of generative AI innovation.
The "OpenAI Development Guide" is a comprehensive, state-of-the-art resource for engineers, architects, researchers, and technology leaders seeking to master development with OpenAI's pioneering artificial intelligence tools. It begins with a thoughtful exploration of OpenAI's history and philosophy, providing essential insights into the evolution, architecture, and taxonomy of its large language models. Readers are introduced to foundational concepts such as transformer architectures, training strategies, and the diverse families of OpenAI models-including GPT, DALL-E, and Whisper-setting a robust groundwork for effective, responsible AI development.
Moving from foundational knowledge to practical integration, the guide offers in-depth coverage of OpenAI API programming, advanced integration scenarios, and innovative prompt engineering techniques. It discusses best practices around secure authentication, optimization of API workflows, robust error handling, and cutting-edge methods for harnessing multi-modal capabilities. Detailed chapters on custom model training, fine-tuning workflows, and deployment strategies empower developers to build, evaluate, and optimize domain-specific AI solutions, while maintaining rigorous security, privacy, and ethical standards throughout.
The latter sections focus on real-world considerations and advanced architectural patterns, presenting proven approaches for scaling AI applications, managing operational complexity, and maintaining high performance. Readers will find expert guidance on DevOps, monitoring, observability, and chaos engineering tailored to AI workloads. Case studies and future-facing discussions showcase the transformative power of OpenAI in applications such as conversational agents, document understanding, compliance automation, and knowledge graph enhancement, grounding the technology in both today's best practices and tomorrow's opportunities. This guide is an essential companion for anyone striving to stay at the forefront of generative AI innovation.
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