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Tested strategies to reduce hallucinations, improve performance and cost efficiency, and reduce bias or unethical behavior in your LLMs outputs.
Building Reliable AI Systems shows you exactly how to guide large language models from research prototypes to scalable, robust, and efficient production systems. From model training to maintenance, an engineer will find everything they need to work with LLMs in this one-stop guide.
Inside Building Reliable AI Systems you’ll learn how to:
Building Reliable AI Systems is a guide to putting LLMs into production in the real world. The book bridges the gap between theory and practice. You’ll go beyond basics like prompting into advanced optimizations: intelligent agents, Retrieval Augmented Generation (RAG), and in-depth solutions for mitigating hallucinations and bias.
Building Reliable AI Systems shows you exactly how to guide large language models from research prototypes to scalable, robust, and efficient production systems. From model training to maintenance, an engineer will find everything they need to work with LLMs in this one-stop guide.
Inside Building Reliable AI Systems you’ll learn how to:
- Deploy LLMs into production
- Detect and reduce hallucinations
- Mitigate bias
- Optimize LLM performance and resource usage
- Advanced prompt engineering techniques
- Build intelligent agents and Retrieval-Augmented Generation
Building Reliable AI Systems is a guide to putting LLMs into production in the real world. The book bridges the gap between theory and practice. You’ll go beyond basics like prompting into advanced optimizations: intelligent agents, Retrieval Augmented Generation (RAG), and in-depth solutions for mitigating hallucinations and bias.
about the book
Building Reliable AI Systems is a comprehensive guide to creating LLM-based apps that are faster and more accurate. It takes you from training to production and beyond into the ongoing maintenance of an LLM. In each chapter, you’ll find in-depth code samples and hands-on projects—including building a RAG-powered chatbot and an agent created with LangChain. Deploying an LLM can be costly, so you’ll love the performance optimization techniques—prompt optimization, model compression, and quantization—that make your LLMs quicker and more efficient. Throughout, real-world case studies from e-commerce, healthcare, and legal work give concrete examples of how businesses have solved some of LLMs common problems.about the author
Rush Shahani is a seasoned AI Engineer and CTO of Persana AI, a YCombinator-backed startup. At Persana, he leads the development of natural language processing and large language model systems that provide actionable insights to companies in order to drive revenue growth. His experience includes building AI systems at companies like LinkedIn, Element AI, and Shopify.If you have been struggling to get your LLM to be more reliable and eliminate the hallucinations in the response, then look no further. This book will show you all you need to know and then some. The theory mixed with the code examples in jupyter notebook form is really helpful to see things in action.
A must-read for anyone working with large language models! This book offers clear explanations, practical techniques, and real-world insights that bridge the gap between theory and application. Whether you’re a beginner or experienced practitioner, it provides valuable guidance on prompt engineering, RAG, fine-tuning, and deploying AI agents effectively. Highly recommended!
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