Idézet: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.
This book complements Sebastian Raschka's Build a Large Language Model (From Scratch), which takes a hands-on, ground-up approach to constructing LLMs. While Raschka's book focuses on building models from scratch, Building Reliable AI Systems centers on deploying, optimizing, and maintaining reliable, production-grade AI systems.

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.

Idézet:? Building Reliable AI Systems MEAP 10.epub ( welcome.html) (9.44 MB)
? Building Reliable AI Systems MEAP 10.pdf (welcome.html) (8.46 MB)

Idézet:A kódrészlet megtekintéséhez be kell jelentkezned, vagy nincs jogosultságod a tartalom megtekintéséhez.

Idézet:A kódrészlet megtekintéséhez be kell jelentkezned, vagy nincs jogosultságod a tartalom megtekintéséhez.