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Building Business-Ready Generative AI Systems. Build Human-Centered AI Systems with Context Engineering, Agents, Memory, and LLMs for Enterprise Denis Rothman

Język publikacji: angielski
Building Business-Ready Generative AI Systems. Build Human-Centered AI Systems with Context Engineering, Agents, Memory, and LLMs for Enterprise Denis Rothman - okladka książki

Building Business-Ready Generative AI Systems. Build Human-Centered AI Systems with Context Engineering, Agents, Memory, and LLMs for Enterprise Denis Rothman - okladka książki

Autor:
Denis Rothman
Serie wydawnicze:
Expert Insight
Ocena:
Bądź pierwszym, który oceni tę książkę
Stron:
444
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Do przechowalni

Standalone LLMs no longer deliver sufficient business value on their own. This guide moves beyond basic chatbots, showing you how to build agentic, ChatGPT-grade systems capable of sophisticated semantic and sentiment analysis, powered by context engineering.

You'll design AI controller architectures with multi-user memory retention to dynamically adapt your system to diverse user and system inputs. You'll architect a Retrieval-Augmented Generation system with Pinecone to combine instruction-driven scenarios. Through context engineering, you’ll minimize token usage, maximize response quality, and create systems that reason across complex tasks with precision. You'll enhance your system’s intelligence with multimodal capabilities—image generation, voice interactions, and machine-driven reasoning—leveraging Chain-of-Thought and context chaining to address cross-domain automation challenges. You'll also integrate OpenAI’s suite and DeepSeek-R1 without disrupting your existing GenAISys ecosystem.

With context engineering as the backbone, every step becomes a deliberate act of shaping model behavior. Your GenAISys will apply neuroscience-inspired insights to marketing strategies, predict human mobility, integrate smoothly into human workflows, and connect to live external data, all wrapped in a polished, investor-ready interface.

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O autorze książki

Denis Rothman graduated from Sorbonne University and Paris-Diderot University, designing one of the very first word2matrix patented embedding and patented AI conversational agents. He began his career authoring one of the first AI cognitive natural language processing (NLP) chatbots applied as an automated language teacher for Moët et Chandon and other companies. He authored an AI resource optimizer for IBM and apparel producers. He then authored an advanced planning and scheduling (APS) solution used worldwide.

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