Building AI Evals: Proven Techniques to Continuously Test, Monitor & Improve LLM Systems.
What’s the one thing that separates an AI system you can trust from one you hope won’t break? It’s not the number of parameters, the size of the dataset, or the flashiest benchmark scores—it’s the discipline of relentless, real-world evaluation.
Building AI Evals is the developer’s guide to making large language models robust, auditable, and production-ready. Written with hands-on energy, this book equips you to move beyond one-off tests and static metrics. Whether you’re refining retrieval-augmented generation pipelines, integrating agents with complex tool use, or deploying LLMs at scale, this book gives you practical frameworks to build continuous, automated, and actionable evaluation systems from the ground up.
Cut through the noise and tackle real engineering challenges:
Design golden datasets that adapt as your product evolves
Implement rigorous, reproducible evaluation pipelines with proven open-source tools
Monitor cost, quality, and safety metrics that matter in real production environments
Automate judge logic, rubric scoring, and red-team sweeps to catch failures before users do
Integrate CI/CD for fast, auditable feedback on every change
Transform production failures into golden test cases for continuous improvement
Inside, you’ll master field-tested techniques for:
Setting up evaluation harnesses that actually scale
Writing and calibrating rubrics as code
Slicing and dashboarding observability data to guide development
Keeping your release process audit-ready and cost-efficient
Applying lessons from real-world case studies—including support automation, contract review, and fail-safe enterprise deployment
Are you ready to build LLM systems that perform, improve, and stand up to scrutiny?
Take the step from hopeful launches to confident releases—grab your copy of Building AI Evals and start engineering with certainty today.
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Paperback. Zustand: new. Paperback. Building AI Evals: Proven Techniques to Continuously Test, Monitor & Improve LLM Systems.What's the one thing that separates an AI system you can trust from one you hope won't break? It's not the number of parameters, the size of the dataset, or the flashiest benchmark scores-it's the discipline of relentless, real-world evaluation.Building AI Evals is the developer's guide to making large language models robust, auditable, and production-ready. Written with hands-on energy, this book equips you to move beyond one-off tests and static metrics. Whether you're refining retrieval-augmented generation pipelines, integrating agents with complex tool use, or deploying LLMs at scale, this book gives you practical frameworks to build continuous, automated, and actionable evaluation systems from the ground up.Cut through the noise and tackle real engineering challenges: Design golden datasets that adapt as your product evolvesImplement rigorous, reproducible evaluation pipelines with proven open-source toolsMonitor cost, quality, and safety metrics that matter in real production environmentsAutomate judge logic, rubric scoring, and red-team sweeps to catch failures before users doIntegrate CI/CD for fast, auditable feedback on every changeTransform production failures into golden test cases for continuous improvementInside, you'll master field-tested techniques for: Setting up evaluation harnesses that actually scaleWriting and calibrating rubrics as codeSlicing and dashboarding observability data to guide developmentKeeping your release process audit-ready and cost-efficientApplying lessons from real-world case studies-including support automation, contract review, and fail-safe enterprise deploymentAre you ready to build LLM systems that perform, improve, and stand up to scrutiny?Take the step from hopeful launches to confident releases-grab your copy of Building AI Evals and start engineering with certainty today. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. Bestandsnummer des Verkäufers 9798273238084
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Paperback. Zustand: new. Paperback. Building AI Evals: Proven Techniques to Continuously Test, Monitor & Improve LLM Systems.What's the one thing that separates an AI system you can trust from one you hope won't break? It's not the number of parameters, the size of the dataset, or the flashiest benchmark scores-it's the discipline of relentless, real-world evaluation.Building AI Evals is the developer's guide to making large language models robust, auditable, and production-ready. Written with hands-on energy, this book equips you to move beyond one-off tests and static metrics. Whether you're refining retrieval-augmented generation pipelines, integrating agents with complex tool use, or deploying LLMs at scale, this book gives you practical frameworks to build continuous, automated, and actionable evaluation systems from the ground up.Cut through the noise and tackle real engineering challenges: Design golden datasets that adapt as your product evolvesImplement rigorous, reproducible evaluation pipelines with proven open-source toolsMonitor cost, quality, and safety metrics that matter in real production environmentsAutomate judge logic, rubric scoring, and red-team sweeps to catch failures before users doIntegrate CI/CD for fast, auditable feedback on every changeTransform production failures into golden test cases for continuous improvementInside, you'll master field-tested techniques for: Setting up evaluation harnesses that actually scaleWriting and calibrating rubrics as codeSlicing and dashboarding observability data to guide developmentKeeping your release process audit-ready and cost-efficientApplying lessons from real-world case studies-including support automation, contract review, and fail-safe enterprise deploymentAre you ready to build LLM systems that perform, improve, and stand up to scrutiny?Take the step from hopeful launches to confident releases-grab your copy of Building AI Evals and start engineering with certainty today. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. Bestandsnummer des Verkäufers 9798273238084
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Anbieter: Rarewaves.com UK, London, Vereinigtes Königreich
Paperback. Zustand: New. Bestandsnummer des Verkäufers LU-9798273238084
Anzahl: Mehr als 20 verfügbar