LLM and AGENT AI

Agent AI is a knowledge-driven framework that integrates Large Language Models (LLMs), task automation, and memory-enhanced decision-making. This book explores how AI agents operate, how they interact with external data sources, and the role of retrieval-augmented generation (RAG), fine-tuning, and privacy-enhancing techniques.

The guide provides a deep dive into the core technical aspects behind AI agents and their use cases in enterprise, research, and secure environments.

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Related Topics

• Fundamentals of Large Language Models
  • Introduction to Large Language Models
  • Understanding Transformer Architecture
  • Training Large Language Models
  • Security and Privacy in LLMs
  • Use Cases of LLMs in Enterprise AI
  • Best Practices for Working with LLMs
  • Conclusion
• Fine-Tuning: Optimizing Large Language Models
  • Introduction to Fine-Tuning
  • Methods of Fine-Tuning
  • Data Requirements and Preparation
  • Fine-Tuning Frameworks and Tools
  • Practical Use Cases
  • Best Practices for Fine-Tuning
  • Conclusion
• Retrieval-Augmented Generation (RAG)
  • Introduction to RAG
  • How RAG Works
  • Architectures of RAG
  • Limitations of RAG
  • Best Practices for Implementing RAG
  • Conclusion
• Agentic AI: The Next Frontier in Intelligent Automation
  • Introduction to Agentic AI
  • The Mechanics of Agentic AI
  • Haystack: Building Agentic AI Systems
  • Langchain: Empowering LLM Applications
  • Agentic AI in Financial Services: Use Cases
  • Ethical Considerations and Security
  • The Future of Agentic AI
  • Conclusion
• Memory and Context in Large Language Models
  • Understanding Context in LLMs
  • Managing Context Windows
  • LoreBook: Structured Short-Term Memory
  • Retrieval-Augmented Generation (RAG)
  • Memory Strategies for AI Agents
  • Optimizing Context Usage for LLM-Based Systems
  • Conclusion
• Privacy in Agentic AI Systems
  • Introduction to Privacy in AI Agents
  • Local LLMs: Privacy-Preserving AI
  • Encrypted Dialog Storage and Processing
  • Private Set Intersection (PSI) for Memory & Summarization
  • Anonymization of Unstructured Data
  • Secure AI Collaboration and Data Governance
  • Conclusion

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Contents Overview:

LLM Basics: Understanding the fundamentals of Large Language Models (LLMs), including training, architecture, hyperparameters, and security considerations.

Fine-Tuning: Exploring parameter-efficient fine-tuning (PEFT), full fine-tuning, and domain-specific model adaptation for enterprise applications.

Retrieval-Augmented Generation (RAG): How RAG improves LLM responses by integrating real-time document retrieval, vector databases, and memory caching.

Agent Framework: The architecture of AI agents, including decision-making workflows, planning mechanisms, and tool integrations (Haystack, Langchain).

Memory & Context Management: How agents manage memory, adjust context windows, use long-term memory (LoreBook), and optimize short-term vs. persistent knowledge.

Privacy in AI Agents: The challenges and solutions in privacy-preserving AI, including local LLMs, encrypted conversation logs, federated learning, and confidential AI.

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🌍 Why Agent AI?

• AI agents go beyond chatbots—they act autonomously, retrieve real-time data, and execute complex decision-making workflows.

• Integration of RAG, fine-tuning, and privacy mechanisms makes AI agents powerful yet secure.

• Enterprises can customize and deploy AI agents while maintaining control over data and security.

Start exploring each chapter to build and optimize AI agents for real-world applications!