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Advanced AI: Building Reliable AI Systems
Paperback
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Publisher: Independently Published
Pages: 210
Weight: 1.10
Height: 0.44 Width: 8.50 Depth: 11.00
Language: English
Stop building AI prototypes that impress in demos but fail under real-world pressure. Learn how to architect, evaluate, secure, and operate AI systems that deserve trust in production.
Many AI books teach you how to call a model, write prompts, or connect an API.
ADVANCED AI: Building Reliable AI Systems begins where those tutorials usually stop: what happens when an AI system must survive real users, uncertain outputs, changing knowledge, access boundaries, operational failures, and production accountability?Written by Enterprise & AI Architect Brij Vinod Somanchi, this book follows one internal knowledge and support assistant from a vague business brief all the way to a defensible production-readiness decision. Rather than treating reliability as a final checklist, the book builds evidence chapter by chapter: diagnosis, grounding, evaluation, tool authority, access control, privacy, resilience, versioning, observability, rollback, red-teaming, incident response, and final operational handoff.
Inside, you'll learn how to:
- DIAGNOSE: Decide whether AI is the right intervention at all, define measurable success, establish a non-AI baseline, and draw clear human and deterministic boundaries before architecture begins.
- GROUND: Build retrieval-backed systems that connect model responses to current, authoritative, and verifiable evidence instead of relying on fluent guesswork.
- EVALUATE: Design repeatable test sets, quality gates, behavioral checks, and production-readiness criteria that turn confidence into evidence.
- CONTROL: Bound tool authority, enforce access controls, protect sensitive data, and ensure the system can only see and do what it is actually entitled to.
- ENGINEER FOR REALITY: Prepare for load, failure, stale knowledge, regressions, dependency changes, and the operational conditions that expose weaknesses hidden by a successful demo.
- OBSERVE AND RECOVER: Use monitoring, versioning, rollback strategies, incident practices, and red-team exercises to detect failure early and recover deliberately.
- PROVE READINESS: Assemble a cumulative Production Readiness Dossier that captures the decisions, evidence, limitations, ownership, and rollback paths required to defend a launch honestly.
Throughout the book, the same system evolves across four depth routes - Foundation, Explorer, Builder, and Innovator - allowing readers to move from clear decision framing to implementation, architecture, operating judgment, and defensible technical leadership.
Whether you are an AI engineer, enterprise architect, platform architect, engineering leader, or technical decision-maker, Advanced AI gives you more than implementation patterns. It gives you the discipline to explain why a system works, where it should not be trusted, how you will know when it fails, and who owns the response when it does.
Build one system. Keep the evidence. Make every production claim harder to fake.
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