[{"data":1,"prerenderedAt":219},["ShallowReactive",2],{"navigation_docs_en":3,"\u002Fen\u002Fai-engineering\u002Fintro\u002Fch015-summary":77,"\u002Fen\u002Fai-engineering\u002Fintro\u002Fch015-summary-surround":214},[4],{"title":5,"icon":6,"path":7,"stem":8,"children":9,"page":45},"AI Engineering",null,"\u002Fen\u002Fai-engineering","en\u002F1.ai-engineering",[10,46],{"title":11,"icon":12,"path":13,"stem":14,"children":15,"page":45},"Introduction to Building AI Applications with Foundation Models","i-lucide-brain-circuit","\u002Fen\u002Fai-engineering\u002Fintro","en\u002F1.ai-engineering\u002F1.intro",[16,20,25,30,35,40],{"title":11,"path":17,"stem":18,"icon":19},"\u002Fen\u002Fai-engineering\u002Fintro\u002Fch01","en\u002F1.ai-engineering\u002F1.intro\u002Fch01","i-lucide-sparkles",{"title":21,"path":22,"stem":23,"icon":24},"The Rise of AI Engineering","\u002Fen\u002Fai-engineering\u002Fintro\u002Fch011-the-rise-of-ai-engineering","en\u002F1.ai-engineering\u002F1.intro\u002Fch011-the-rise-of-ai-engineering","i-lucide-history",{"title":26,"path":27,"stem":28,"icon":29},"Foundation Model Use Cases","\u002Fen\u002Fai-engineering\u002Fintro\u002Fch012-foundation-model-use-cases","en\u002F1.ai-engineering\u002F1.intro\u002Fch012-foundation-model-use-cases","i-lucide-layout-grid",{"title":31,"path":32,"stem":33,"icon":34},"Planning AI Applications","\u002Fen\u002Fai-engineering\u002Fintro\u002Fch013-planning-ai-applications","en\u002F1.ai-engineering\u002F1.intro\u002Fch013-planning-ai-applications","i-lucide-clipboard-list",{"title":36,"path":37,"stem":38,"icon":39},"The AI Engineering Stack","\u002Fen\u002Fai-engineering\u002Fintro\u002Fch014-the-ai-engineering-stack","en\u002F1.ai-engineering\u002F1.intro\u002Fch014-the-ai-engineering-stack","i-lucide-layers",{"title":41,"path":42,"stem":43,"icon":44},"Summary","\u002Fen\u002Fai-engineering\u002Fintro\u002Fch015-summary","en\u002F1.ai-engineering\u002F1.intro\u002Fch015-summary","i-lucide-flag",false,{"title":47,"icon":6,"path":48,"stem":49,"children":50,"page":45},"Understanding Foundation Models","\u002Fen\u002Fai-engineering\u002Funderstanding-foundation-models","en\u002F1.ai-engineering\u002F2.understanding-foundation-models",[51,54,59,64,69,74],{"title":47,"path":52,"stem":53,"icon":12},"\u002Fen\u002Fai-engineering\u002Funderstanding-foundation-models\u002Fch02","en\u002F1.ai-engineering\u002F2.understanding-foundation-models\u002Fch02",{"title":55,"path":56,"stem":57,"icon":58},"Training Data","\u002Fen\u002Fai-engineering\u002Funderstanding-foundation-models\u002Fch02-1-training-data","en\u002F1.ai-engineering\u002F2.understanding-foundation-models\u002Fch02-1-training-data","i-lucide-database",{"title":60,"path":61,"stem":62,"icon":63},"Modeling","\u002Fen\u002Fai-engineering\u002Funderstanding-foundation-models\u002Fch02-2-modeling","en\u002F1.ai-engineering\u002F2.understanding-foundation-models\u002Fch02-2-modeling","i-lucide-network",{"title":65,"path":66,"stem":67,"icon":68},"Post-Training","\u002Fen\u002Fai-engineering\u002Funderstanding-foundation-models\u002Fch02-3-post-training","en\u002F1.ai-engineering\u002F2.understanding-foundation-models\u002Fch02-3-post-training","i-lucide-sliders-horizontal",{"title":70,"path":71,"stem":72,"icon":73},"Sampling","\u002Fen\u002Fai-engineering\u002Funderstanding-foundation-models\u002Fch02-4-sampling","en\u002F1.ai-engineering\u002F2.understanding-foundation-models\u002Fch02-4-sampling","i-lucide-dices",{"title":41,"path":75,"stem":76,"icon":44},"\u002Fen\u002Fai-engineering\u002Funderstanding-foundation-models\u002Fch02-5-summary","en\u002F1.ai-engineering\u002F2.understanding-foundation-models\u002Fch02-5-summary",{"id":78,"title":41,"body":79,"description":208,"extension":209,"links":6,"meta":210,"navigation":211,"path":42,"seo":212,"stem":43,"__hash__":213},"docs_en\u002Fen\u002F1.ai-engineering\u002F1.intro\u002Fch015-summary.md",{"type":80,"value":81,"toc":203},"minimark",[82,108,113,165,173,177,184,196],[83,84,85,90],"u-page-hero",{},[86,87,89],"template",{"v-slot:title":88},"","Chapter Summary",[86,91,92,101],{"v-slot:description":88},[93,94,95,96,100],"p",{},"This chapter had ",[97,98,99],"strong",{},"two purposes",": explain the emergence of AI engineering as a discipline thanks to the availability of foundation models, and give an overview of the process needed to build applications on top of these models.",[93,102,103,104,107],{},"As an overview chapter, it only ",[97,105,106],{},"lightly touched"," on many concepts. These will be explored further in the rest of the book.",[109,110,112],"h2",{"id":111},"what-this-chapter-covered","What This Chapter Covered",[114,115,116,137,145,154],"card-group",{},[117,118,120,121,124,125,128,129,132,133,136],"card",{"icon":24,"title":119},"The Rapid Evolution of AI","The transition from language models to ",[97,122,123],{},"large language models",", thanks to a training approach called ",[97,126,127],{},"self-supervision",". Then how language models incorporated other data modalities to become ",[97,130,131],{},"foundation models",", and how foundation models gave rise to ",[97,134,135],{},"AI engineering",".",[117,138,141,142,136],{"icon":139,"title":140},"i-lucide-grid","Application Patterns","The rapid growth of AI engineering is motivated by the many applications enabled by emerging capabilities. The chapter discussed some of the most successful application patterns, both for ",[97,143,144],{},"consumers and enterprises",[117,146,149,150,153],{"icon":147,"title":148},"i-lucide-circle-help","Should You Build It?","Before building an application, an important yet often overlooked question is ",[97,151,152],{},"whether you should build it",". The chapter discussed this question alongside major considerations for building AI applications.",[117,155,156,157,160,161,164],{"icon":39,"title":36},"AI engineering evolved out of ",[97,158,159],{},"ML engineering",". Many ML principles still apply, but AI engineering brings ",[97,162,163],{},"new challenges and solutions",". The last section discussed how the stack has changed.",[166,167,168,169,172],"note",{},"Despite the incredible number of AI applications already in production, ",[97,170,171],{},"we're still in the early stages of AI engineering",", with countless more innovations yet to be built.",[109,174,176],{"id":175},"navigating-the-overwhelm","Navigating the Overwhelm",[93,178,179,180,183],{},"One aspect of AI engineering that is especially challenging to capture in writing is the incredible amount of ",[97,181,182],{},"collective energy, creativity, and engineering talent"," that the community brings. This collective enthusiasm can often be overwhelming, as it's impossible to keep up-to-date with new techniques, discoveries, and engineering feats that seem to happen constantly.",[185,186,187,188,191,192,195],"tip",{},"One consolation is that since AI is great at information aggregation, it can ",[97,189,190],{},"help us aggregate and summarize all these new updates",". But tools can help only to a certain extent. The more overwhelming a space is, the more important it is to have a ",[97,193,194],{},"framework to help us navigate it",". This book aims to provide such a framework.",[93,197,198,199,202],{},"The rest of the book will explore this framework step-by-step, starting with the fundamental building block of AI engineering: ",[97,200,201],{},"the foundation models"," that make so many amazing applications possible.",{"title":88,"searchDepth":204,"depth":204,"links":205},2,[206,207],{"id":111,"depth":204,"text":112},{"id":175,"depth":204,"text":176},"A recap of how foundation models gave rise to AI engineering, the application patterns enabled, and the framework this book provides.","md",{},{"icon":44},{"title":41,"description":208},"WSNrhhPNPoYvadLsD1Yhd56OU9gJn0QlCHzRLyxjXyg",[215,217],{"title":36,"path":37,"stem":38,"description":216,"icon":39,"children":-1},"The three layers of the AI stack, how AI engineering differs from ML engineering and full-stack development, and how foundation models reshape model and application development.",{"title":47,"path":52,"stem":53,"description":218,"icon":12,"children":-1},"A guide to how training data, architecture, size, post-training, and sampling shape foundation model behavior.",1779363441968]