# AI Systems Development using LLMs Dr. Muralidhar Kurni, Dr. Srinidhi Hiriyannaiah

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- Autorzy: [Muralidhar Kurni](https://www.kolczanksiazek.pl/muralidhar-kurni), [Srinidhi Hiriyannaiah](https://www.kolczanksiazek.pl/srinidhi-hiriyannaiah), [Srinivasa K. G.](https://www.kolczanksiazek.pl/srinivasa-k-g)
- Język: pl_PL
- Gatunki: Bazy danych, Pozyskiwanie i analiza danych, Sztuczna inteligencja, Języki naturalne i tłumaczenie automatyczne, Uczenie maszynowe / Systemy uczące się

## Opis

Description Large language models have rapidly transformed artificial intelligence, natural language processing, and modern machine learning, making them essential tools for modern software development. This book is your practical roadmap to understanding these technologies and building intelligent, real-world AI applications. This book systematically covers the essential concepts needed to build, scale, and secure intelligent systems. It begins with the foundations of large language models, scaling laws, ethical considerations, and developer tools like LangChain, LlamaIndex, and Ollama. Next, it guides you through prompt engineering, reasoning techniques, and semantic vector search using custom embeddings. You will then learn how to design advanced retrieval-augmented generation (RAG) systems with vector databases like ChromaDB, FAISS, and Weaviate, alongside conversational memory and evaluation frameworks. Finally, the book covers autonomous AI agent orchestration, multimodal models such as GPT-4o and LLaVA, cloud-to-edge deployment, optimization techniques like LoRA and quantization, prompt security, and emerging standards such as the model context protocol. By the end of this book, readers will be well-equipped to design, deploy, and secure production-grade AI systems, possessing the practical skills required to solve complex real-world engineering challenges. They will learn to build scalable, secure, and trustworthy LLM applications for academic, industrial, and entrepreneurial settings. What you will learn Master LLM foundations, scaling, and LangChain ecosystem framework tools. Orchestrate multi-agent systems with ReAct workflows and safety interlocks. Build conversational and RAG applications using vector databases and evaluation frameworks. Develop, orchestrate, govern, and secure task-oriented AI agents. Deploy and optimize scalable, secure, and multimodal LLM applications. Implement semantic chunking across ChromaDB, FAISS, and Weaviate databases. Who this book is for This book is intended for undergraduate and postgraduate students, AI/ML engineers, data scientists, software developers, researchers, system architects, cloud engineers, educators, startup teams, and professionals seeking to build practical applications using large language models. Basic familiarity with Python programming and introductory machine learning concepts is recommended. Table of Contents 1. Introduction to Large Language Models 2. LLM Tools and Frameworks 3. Open-source LLM Ecosystem 4. Ethics, Bias, and Responsible AI 5. Fundamentals of Prompt Engineering 6. Advanced Prompt Engineering Techniques 7. Embeddings and Semantic Search 8. Fine-tuning and Customizing Embeddings 9. Chunking Strategies and Vector Databases 10. Conversational Applications with LLMs 11. Designing Retrieval-augmented Generation Systems 12. Advanced RAG and Explainability 13. Building Foundations of AI Agents 14. Orchestrating and Securing AI Agents 15. Deployment and Optimization of LLM Systems 16. Security and Safety in LLM Applications 17. Multimodal LLMs for Text, Vision, and Speech 18. Future of AI Systems with LLMs

## Wydania

### AI Systems Development using LLMs Dr. Muralidhar Kurni, Dr. Srinidhi Hiriyannaiah

- Wydawnictwo: BPB Publications (Z chęcią przeczytam książkę w języku polskim)
- ISBN: 978-81-6708-040-0
- Liczba stron: 460
- Język: pl_PL