[{"data":1,"prerenderedAt":282},["ShallowReactive",2],{"navigation_docs_en":3,"\u002Fen\u002Fai-engineering\u002Fevaluate-ai-systems\u002Fch04-4-summary":141,"\u002Fen\u002Fai-engineering\u002Fevaluate-ai-systems\u002Fch04-4-summary-surround":279},[4],{"title":5,"icon":6,"path":7,"stem":8,"children":9,"page":45},"AI Engineering",null,"\u002Fen\u002Fai-engineering","en\u002F1.ai-engineering",[10,46,77,114],{"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 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Evaluation","\u002Fen\u002Fai-engineering\u002Fevaluation-methodology\u002Fch03-5-ranking-models-with-comparative-evaluation","en\u002F1.ai-engineering\u002F3.evaluation-methodology\u002Fch03-5-ranking-models-with-comparative-evaluation","i-lucide-trophy",{"title":41,"path":112,"stem":113,"icon":44},"\u002Fen\u002Fai-engineering\u002Fevaluation-methodology\u002Fch03-6-summary","en\u002F1.ai-engineering\u002F3.evaluation-methodology\u002Fch03-6-summary",{"title":115,"icon":116,"path":117,"stem":118,"children":119,"page":45},"Evaluate AI Systems","i-lucide-binary","\u002Fen\u002Fai-engineering\u002Fevaluate-ai-systems","en\u002F1.ai-engineering\u002F4.evaluate-ai-systems",[120,123,128,133,138],{"title":115,"path":121,"stem":122,"icon":116},"\u002Fen\u002Fai-engineering\u002Fevaluate-ai-systems\u002Fch04","en\u002F1.ai-engineering\u002F4.evaluate-ai-systems\u002Fch04",{"title":124,"path":125,"stem":126,"icon":127},"Evaluation Criteria","\u002Fen\u002Fai-engineering\u002Fevaluate-ai-systems\u002Fch04-1-evaluation-criteria","en\u002F1.ai-engineering\u002F4.evaluate-ai-systems\u002Fch04-1-evaluation-criteria","i-lucide-check-circle-2",{"title":129,"path":130,"stem":131,"icon":132},"Model Selection","\u002Fen\u002Fai-engineering\u002Fevaluate-ai-systems\u002Fch04-2-model-selection","en\u002F1.ai-engineering\u002F4.evaluate-ai-systems\u002Fch04-2-model-selection","i-lucide-cpu",{"title":134,"path":135,"stem":136,"icon":137},"Design Your Evaluation Pipeline","\u002Fen\u002Fai-engineering\u002Fevaluate-ai-systems\u002Fch04-3-design-your-evaluation-pipeline","en\u002F1.ai-engineering\u002F4.evaluate-ai-systems\u002Fch04-3-design-your-evaluation-pipeline","i-lucide-workflow",{"title":41,"path":139,"stem":140,"icon":44},"\u002Fen\u002Fai-engineering\u002Fevaluate-ai-systems\u002Fch04-4-summary","en\u002F1.ai-engineering\u002F4.evaluate-ai-systems\u002Fch04-4-summary",{"id":142,"title":41,"body":143,"description":273,"extension":274,"links":6,"meta":275,"navigation":276,"path":139,"seo":277,"stem":140,"__hash__":278},"docs_en\u002Fen\u002F1.ai-engineering\u002F4.evaluate-ai-systems\u002Fch04-4-summary.md",{"type":144,"value":145,"toc":268},"minimark",[146,157,162,223,227],[147,148,149,154],"u-page-hero",{},[150,151,153],"template",{"v-slot:title":152},"","Chapter Summary",[150,155,156],{"v-slot:description":152},"Reliable evaluation is the single greatest catalyst for real-world AI adoption. While constructing evaluation pipelines demands up-front discipline, it mitigates catastrophic risks, accelerates iteration, and grounds development in quantifiable business value.",[158,159,161],"h2",{"id":160},"core-chapter-takeaways","Core Chapter Takeaways",[163,164,165,187,195,211],"card-group",{},[166,167,169,170,174,175,178,179,182,183,186],"card",{"icon":127,"title":168},"Application-Driven Criteria","Evaluation must always be grounded in specific application requirements. Criteria span ",[171,172,173],"strong",{},"domain-specific capabilities",", ",[171,176,177],{},"generation quality"," (factual consistency and safety), ",[171,180,181],{},"instruction-following fidelity",", and ",[171,184,185],{},"cost\u002Flatency economics",".",[166,188,190,191,194],{"icon":105,"title":189},"Build vs. Buy Trade-offs","Choosing between proprietary model APIs and self-hosting open weights requires evaluating ",[171,192,193],{},"seven axes",": data privacy, data lineage, capability frontier, tooling features, cost curves, version control, and on-device requirements.",[166,196,199,200,174,203,206,207,210],{"icon":197,"title":198},"i-lucide-alert-triangle","Benchmark Realism & Contamination","Public benchmarks help filter out weak models but cannot identify the best candidate for your workload. Beware of ",[171,201,202],{},"unweighted averages",[171,204,205],{},"benchmark correlation",", and widespread ",[171,208,209],{},"data contamination"," in web-scraped training sets.",[166,212,214,215,218,219,222],{"icon":137,"title":213},"Robust Pipeline Architecture","Evaluate systems across ",[171,216,217],{},"components, turns, and overarching tasks",". Use ",[171,220,221],{},"slice-based evaluation"," to avoid Simpson's paradox, establish clear scoring rubrics, and calibrate automated evaluators against business KPIs.",[158,224,226],{"id":225},"looking-ahead","Looking Ahead",[228,229,230,234],"note",{},[231,232,233],"p",{},"Evaluation is not a one-time milestone; it is an ongoing practice that underpins every phase of AI engineering:",[235,236,237,244,250,256,262],"ul",{},[238,239,240,243],"li",{},[171,241,242],{},"Chapter 5",": Prompt engineering and advanced reasoning techniques.",[238,245,246,249],{},[171,247,248],{},"Chapter 6",": Retrieval-Augmented Generation (RAG) and agentic tool evaluation.",[238,251,252,255],{},[171,253,254],{},"Chapters 7 & 9",": Fine-tuning, quantization, latency optimization, and memory profiling.",[238,257,258,261],{},[171,259,260],{},"Chapter 8",": Synthetic data generation and automated quality verification.",[238,263,264,267],{},[171,265,266],{},"Chapter 10",": Production observability, continuous monitoring, and user feedback loops.",{"title":152,"searchDepth":269,"depth":269,"links":270},2,[271,272],{"id":160,"depth":269,"text":161},{"id":225,"depth":269,"text":226},"A recap of system evaluation principles, model selection trade-offs, and practical guidelines for building production evaluation harnesses.","md",{},{"icon":44},{"title":41,"description":273},"AhR1l51ah2pwjsYyU7vWzJDIujxWPRXk8_RMsH05N38",[280,6],{"title":134,"path":135,"stem":136,"description":281,"icon":137,"children":-1},"A practical guide to architecting production evaluation pipelines, defining scoring rubrics, slicing datasets, and avoiding Simpson's paradox.",1789413992697]