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Build safe, reliable, production-ready AI applications that you can trust. This four-chapter ebook gives you a practical look at the risks that can undermine an AI application as well as the methods used to address them. Examine how systems are exploited through adversarial attacks, evaluate hallucinations with structured detection techniques, learn how to use guardrails to control the flow of information and actions, and see what it takes to deploy and operate an LLM service. By following this curated learning path, you’ll build a stronger foundation for testing AI behavior, making informed design choices, and taking on more advanced work in AI safety, evaluation, agent design, and production engineering.
about the authors
Numa Dhamani is a natural language processing expert working at the intersection of technology and society. Maggie Engler is a researcher and engineer working on safety for generative AI systems.Han Lee has spent more than a decade applying cutting-edge research on large-scale AI and machine learning systems into production‐grade products. A Senior Director of Data and AI at Moody’s, he leads teams that ship generative AI applications and has daily, hands-on exposure to safety-critical evaluation pipelines.
Peter Belcak is an AI Research Scientist, currently at NVIDIA Research. Prior to that he was at Meta GenAI and ETH Zurich. He has contributed across the entire AI stack, from AI efficiency, through efficient model training and tuning frameworks to NVIDIA’s Enterprise AI agent frameworks and products. He currently researches methods for improving the reliability of agentic systems and the formation of logically correct reasoning systems, and leads product efforts on LLM hallucination elimination and LLM advertising.
Christopher Brousseau and Matt Sharp are experienced engineers who have led numerous successful large scale LLM deployments.
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Trustworthy Generative AI ebook for free
Trustworthy Generative AI ebook for free
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Trustworthy Generative AI ebook for free
