Agentic AI Engineering with Generative AI: Designing, Building, and Scaling Goal-Driven, LLM-Powered Systems - Tapa blanda

Agentic AI Engineering with Generative AI (Paperback)

Paperback. Condición: new. Paperback. This comprehensive guide provides a structured approach to engineering agentic AI systems powered by generative AI, covering design, development, and deployment. Key chapters include: Core Concepts and Technologies: Explore frameworks like LangChain and LlamaIndex, hardware requirements, and integration with external tools.Defining Purpose and Scope: Align agents with clear objectives, success metrics, and environmental constraintsChoosing the Right Model: Balance fine-tuning and prompt engineering, manage token limits, and address cost considerationsDevelopment Environment: Set up testing, debugging, and frameworks like Hugging Face TransformersPrompt Engineering: Craft effective prompts, mitigate ambiguity, and refine iteratively for task automationAgentic Workflows: Integrate generative AI with APIs, manage state, and optimize workflowsAutonomy with RL: Enhance agents with reinforcement learning for adaptive decision-makingMulti-Agent Systems: Design collaborative agents with specialized roles and robust communication protocols.Testing and Safety: Evaluate outputs, ensure robustness, and implement ethical guardrailsDeployment and Scaling: Deploy on cloud, on-premise, or edge, with monitoring and maintenance strategiesEnhanced Capabilities: Incorporate multimodal inputs, real-time adaptation, and IoT integration for advanced applications like smart home control.Ethical Design: Mitigate bias, ensure transparency, and comply with regulations like GDPR and HIPAA.By combining LLMs, Stable Diffusion, and next-gen tools, this guide equips developers to build scalable, ethical, and autonomous agents that push the boundaries of AI-driven productivity and creativity. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. Nº de ref. del artículo: 9798266779266

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