![[Review] AI Engineering: Building Applications with Foundation Models (Chip Huyen) Summarized.](https://episodes.castos.com/660078c6833215-59505987/images/2642561/c1a-085k3-xxmpxw9rbv-zzg8ir.jpg)
Show Notes
AI Engineering: Building Applications with Foundation Models (Chip Huyen)
- Amazon USA Store: https://www.amazon.com/dp/1098166302?tag=9natree-20
- Amazon Worldwide Store: https://global.buys.trade/AI-Engineering%3A-Building-Applications-with-Foundation-Models-Chip-Huyen.html
- Apple Books: https://books.apple.com/us/audiobook/ai-engineering-building-applications-with-foundation/id1794381401?itsct=books_box_link&itscg=30200&ls=1&at=1001l3bAw&ct=9natree
- eBay: https://www.ebay.com/sch/i.html?_nkw=AI+Engineering+Building+Applications+with+Foundation+Models+Chip+Huyen+&mkcid=1&mkrid=711-53200-19255-0&siteid=0&campid=5339060787&customid=9natree&toolid=10001&mkevt=1
- Read more: https://english.9natree.com/read/1098166302/
#Foundationmodelapplications #AIasajudge #Retrievalaugmentedgeneration #Inferenceoptimization #AIengineeringarchitecture #AIEngineering
AI Engineering: Building Applications with Foundation Models is a practical technology book by Chip Huyen about creating useful software with large language models and other foundation models. Rather than treating AI development chiefly as the training of custom models, it examines the work of adapting capable pretrained models to products, workflows, and users. The book places this work within an emerging AI engineering discipline shaped by model APIs, open models, rapidly changing capabilities, and operational constraints. Its scope extends beyond prompts: it addresses model selection, evaluation, retrieval augmented generation, fine tuning, agents, dataset engineering, inference performance, system architecture, monitoring, and feedback. Huyen frames these subjects as connected design decisions rather than isolated techniques. The central purpose is to help readers reason about how an AI application can become reliable enough, fast enough, and economical enough for real use. It is therefore a systems oriented guide to the opportunities and limits of building on foundation models, not a narrowly focused programming tutorial or a guide to training frontier models from scratch.
- Amazon USA Store: https://www.amazon.com/dp/1098166302?tag=9natree-20
- Amazon Worldwide Store: https://global.buys.trade/AI-Engineering%3A-Building-Applications-with-Foundation-Models-Chip-Huyen.html
- Apple Books: https://books.apple.com/us/audiobook/ai-engineering-building-applications-with-foundation/id1794381401?itsct=books_box_link&itscg=30200&ls=1&at=1001l3bAw&ct=9natree
- eBay: https://www.ebay.com/sch/i.html?_nkw=AI+Engineering+Building+Applications+with+Foundation+Models+Chip+Huyen+&mkcid=1&mkrid=711-53200-19255-0&siteid=0&campid=5339060787&customid=9natree&toolid=10001&mkevt=1
- Read more: https://english.9natree.com/read/1098166302/
#Foundationmodelapplications #AIasajudge #Retrievalaugmentedgeneration #Inferenceoptimization #AIengineeringarchitecture #AIEngineering
AI Engineering: Building Applications with Foundation Models is a practical technology book by Chip Huyen about creating useful software with large language models and other foundation models. Rather than treating AI development chiefly as the training of custom models, it examines the work of adapting capable pretrained models to products, workflows, and users. The book places this work within an emerging AI engineering discipline shaped by model APIs, open models, rapidly changing capabilities, and operational constraints. Its scope extends beyond prompts: it addresses model selection, evaluation, retrieval augmented generation, fine tuning, agents, dataset engineering, inference performance, system architecture, monitoring, and feedback. Huyen frames these subjects as connected design decisions rather than isolated techniques. The central purpose is to help readers reason about how an AI application can become reliable enough, fast enough, and economical enough for real use. It is therefore a systems oriented guide to the opportunities and limits of building on foundation models, not a narrowly focused programming tutorial or a guide to training frontier models from scratch.
