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8.3 MB
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B9067B9F9408A43BE72518E91FCFC3B1C167AAE9
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Sept. 16, 2025, 11:30 a.m.
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(Last updated: Sept. 16, 2025, 11:32 a.m.)
| File | Size |
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| Shahani R. Building Reliable AI Systems.Production-ready methods..(MEAP V9) 2025.pdf | 8.3 MB |
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SOURCE: Shahani R. Building Reliable AI Systems.Production-ready methods..(MEAP V9) 2025
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COVER
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MEDIAINFO
Textbook in PDF format 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: 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 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. Preface Deploying_reliable_and_responsible_large_language_models_in_the_real_world Understanding_and_measuring_hallucinations_in_LLMs Minimizing_hallucinations_and_enhancing_reliability_with_prompt_engineering_ Advancing_trust_&_minimizing_hallucinations_with_retrieval_augmented_genera Building_Reliable_AI_Agents Performance_optimization_techniques_for_LLMs_and_agents Fine-Tuning_LLMs_for_Improved_Performance Embeddings,_Vector_Databases_and_Retrieval Deploying_and_monitoring_large_language_models_for_high quality_outcomes Bias,_privacy_and_trust_in_AI_systems Model_Context_Protocol_and_multi-agent_AI_systems
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