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Memory-Driven AI: A Deep Dive into AI Memory Systems, Vector Databases, and Persistent Context for Smarter, Scalable, and Reliable AI Applications(The Context Engineering Mastery Series: From Prompt Design, Memory Systems, and Rag to Multi-Agent W)

Memory-Driven AI: A Deep Dive into AI Memory Systems, Vector Databases, and Persistent Context for Smarter, Scalable, and Reliable AI Applications(The Context Engineering Mastery Series: From Prompt Design, Memory Systems, and Rag to Multi-Agent W)

          
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About the Book

What happens when AI doesn't just respond but actually remembers? The future of artificial intelligence lies in systems that retain context, recall knowledge across sessions, and build persistent memory-pushing beyond the limits of today's stateless models.


This book explores the foundations and practical applications of memory-driven AI, showing how embedding pipelines, vector databases, and Retrieval-Augmented Generation (RAG) create smarter, scalable, and more reliable systems. Written for developers, researchers, and AI practitioners, it provides both conceptual clarity and hands-on blueprints for building systems that learn continuously, adapt to scale, and deliver contextually relevant outputs. Whether you are working on conversational AI, enterprise knowledge automation, or multi-agent frameworks, this book equips you with the tools to move from short-term interactions to long-term intelligence.

What sets this book apart?
Unlike general AI guides, this volume focuses specifically on memory as the defining frontier of next-generation systems. Inside, you'll explore:

  • Core foundations: Types of AI memory, persistent context, and the evolution of RAG.

  • Technical depth: Embedding pipelines, vector indexing strategies (IVF, HNSW, PQ), and distributed architectures.

  • Scalable systems: Designing retrieval loops, hybrid search, multi-agent synchronization, and lifelong learning.

  • Practical insights: Real-world applications in customer support, healthcare, and enterprise knowledge automation.

  • Ethics and governance: Data governance, hallucination prevention, and the right to erasure in persistent AI memory.

  • Future directions: Collective memory across distributed systems, neuro-symbolic approaches, and building trustworthy, memory-first AI.

Each chapter blends clear explanations with real-world case studies and Python code illustrations, making the material both accessible and actionable.

If you want to build AI that doesn't just process data but retains, recalls, and evolves with context, this book is your guide. Move beyond stateless models and design systems that scale reliably, ground responses in validated knowledge, and earn user trust. Get your copy today and start building the memory-first AI systems that will define the next era of intelligent applications.


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Product Details
  • ISBN-13: 9798262411979
  • Publisher: Independently Published
  • Publisher Imprint: Independently Published
  • Height: 254 mm
  • No of Pages: 162
  • Series Title: The Context Engineering Mastery Series: From Prompt Design, Memory Systems, and Rag to Multi-Agent W
  • Sub Title: A Deep Dive into AI Memory Systems, Vector Databases, and Persistent Context for Smarter, Scalable, and Reliable AI Applications
  • Width: 178 mm
  • ISBN-10: 8262411978
  • Publisher Date: 26 Aug 2025
  • Binding: Paperback
  • Language: English
  • Returnable: N
  • Spine Width: 9 mm
  • Weight: 290 gr


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Memory-Driven AI: A Deep Dive into AI Memory Systems, Vector Databases, and Persistent Context for Smarter, Scalable, and Reliable AI Applications(The Context Engineering Mastery Series: From Prompt Design, Memory Systems, and Rag to Multi-Agent W)

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