{"product_id":"llm-as-a-judge-for-ai-systems-automated-evaluation-frameworks-bias-controls-and-ci-cd-quality-gates-for-developers-building-reliable-ai-9798298505949","title":"LLM as a Judge for AI Systems: Automated Evaluation Frameworks, Bias Controls, and CI\/CD Quality Gates for Developers Building Reliable AI","description":"\u003cp\u003e • Author(s): Newman Chandler\u003cbr\u003e • Publisher: Independently Published\u003cbr\u003e • Publisher Imprint: Independently Published\u003cbr\u003e • BISAC: Information Theory\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003cb\u003eLLM as a Judge for AI Systems: Automated Evaluation Frameworks, Bias Controls, and CI\/CD Quality Gates for Developers Building Reliable AI\u003c\/b\u003e \u003c\/p\u003e\u003cp\u003e\u003c\/p\u003eStruggling to test AI that never gives the same answer twice, how do you gate releases, stop hallucinations, and measure fairness at scale?\u003cp\u003eThis book gives you a pragmatic answer: treat large language models as repeatable, auditable judges and embed those judges into your engineering lifecycle. LLM as a Judge for AI Systems exposes a hands-on approach to building automated evaluation frameworks, applying bias controls, and enforcing CI\/CD quality gates so teams can ship reliable AI with confidence.\u003c\/p\u003e\u003cp\u003e\u003cb\u003eOverview\u003c\/b\u003e\u003cbr\u003ePractical, code-friendly, and operations-centered, the book shows you how to design rubrics, craft parseable prompts (rubric + CoT + JSON), run pairwise\/listwise evaluations, and integrate judge-driven checks into GitHub Actions and Pytest. It explains bias detection and calibration, contrastive tuning, adversarial red-teaming, and pragmatic governance patterns, so your evaluation is fast, repeatable, and defensible.\u003c\/p\u003e\u003cp\u003e\u003cb\u003eWhat you'll gain?\u003c\/b\u003e\u003c\/p\u003e\u003cul\u003e\n\u003cli\u003e\u003cp\u003eConvert product KPIs into measurable evaluation dimensions (factuality, relevance, tone).\u003c\/p\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cp\u003eBuild regression + adversarial test suites that gate PRs and block regressions.\u003c\/p\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cp\u003eImplement G-Eval-style prompts that produce parsable scores and rationale logs for audits.\u003c\/p\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cp\u003eRun pairwise A\/B pipelines and listwise reranking inside CI, with anonymization and debiasing.\u003c\/p\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cp\u003eDetect and correct judge bias (position, verbosity, self-enhancement) using calibration tools.\u003c\/p\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cp\u003eHarden evaluation against prompt-injection and gaming with sanitation, auditor passes, and red teams.\u003c\/p\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cp\u003eOperationalize human fallback, multi-judge consensus, and re-playable audit trails for compliance.\u003c\/p\u003e\u003c\/li\u003e\n\u003c\/ul\u003e\u003cp\u003e\u003cb\u003eWho should buy it?\u003c\/b\u003e\u003cbr\u003eEngineers, ML-ops, product leaders, and safety reviewers who build or ship LLM-powered products and need a reproducible, production-grade evaluation lifecycle.\u003c\/p\u003e\u003cp\u003e\u003cb\u003eReady to make evaluation part of your delivery loop and ship AI you can trust? Purchase LLM as a Judge for AI Systems and get the playbooks, prompts, and CI patterns you can drop into your repo today.\u003c\/b\u003e\u003c\/p\u003e","brand":"Independently Published","offers":[{"title":"Paperback","offer_id":46863076688023,"sku":"9798298505949","price":1621.0,"currency_code":"INR","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0666\/3471\/1191\/files\/9798298505949.webp?v=1769968754","url":"https:\/\/atlanticbooks.com\/products\/llm-as-a-judge-for-ai-systems-automated-evaluation-frameworks-bias-controls-and-ci-cd-quality-gates-for-developers-building-reliable-ai-9798298505949","provider":"Atlantic Books","version":"1.0","type":"link"}