book

The Value Vector: Building Scalable Generative AI-Based Applications

Name: The Value Vector: Building Scalable Generative AI-Based Applications
ISBN: 9798868818806

by Jaydeep Chakrabarty, Harinee Muralinath

October 2025

Intermediate to advanced

302 pages

7h 14m

English

Apress

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The Value Vector
Prologue
How to Read This BookWhy Include a Story?Understanding the StructureHow to Approach Each ChapterThe Unique Nature of AIWho Should Read This BookFinal Thoughts
Table of Contents
About the Authors
About the Technical Reviewer
1. The Value Compass – Navigating the AI Hype
Why Business Value MattersEscaping the “Death by POC” trapFrom Idea to Real-World Use: A Generative AI Implementation WorkflowStep 1: Senior Leadership EducationStep 2: Use Case IdeationStep 3: Stakeholder WorkshopStep 4: PrioritizationStep 5: Rapid POC DevelopmentStep 6: MVP CreationStep 7: Deployment and MeasurementAnya’s NotesConcepts in PracticeThe AI Value Compass: A Strategic Framework for AI ImplementationVector 1: Business Challenge IdentificationVector 2: AI Capability MappingVector 3: Value and Impact AssessmentVector 4: Ethical Implementation FeasibilityImplementing the AI Value CompassNavigating the InitiativesFramework for Evaluating AI Initiatives: The Degree of UnknownsAvoiding the “Death by POC” TrapThe Generative AI Implementation Workflow: Charting Your Course to AI SuccessKey learnings from this chapter
2. The GenAI Idea Maze – Finding Gold Among the Glitter
Inbox Overflow: The GenAI Proposal DelugeMind the Gap: Educating the OrganizationMapping the AI LandscapeThe Use Case Detective’s ToolkitAnya’s NotesConcepts in PracticeFirst Steps into the AI WorldThree Waves of InnovationThe Data DilemmaMind the Gap: Cultivating AI UnderstandingThe Need for AI LiteracyMaking AI Education Engaging and PracticalUnderstanding AI’s Core CapabilitiesFrom Capabilities to Business SolutionsThe Use Case Detective’s ToolkitThe Strategic Foundation: “Should We Build?”The Tactical Roadmap: “How Do We Build?”The Framework in Action: Transforming Fashion RetailStarting with “Should We Build?”Uncovering the Real ProblemUnderstanding Current WorkflowsEnvisioning the SolutionResponsible AI ConsiderationsOrganizational ReadinessMoving to “How Do We Build?”Defining SuccessData FoundationBuilding for ScaleAdoption StrategyFramework InsightsReal ImpactWhen Theory Meets RealityKey Learnings from This chapter
3. Proof or Dare – Reimagining POCs in the GenAI Playground
POCs: Then and NowBlueprint for GenAI POCsFrom Architecture to Action: Implementing and Demonstrating GenAI POCsAnya’s NotesConcepts in PracticeUnderstanding the Evolution of POCs in the AI EraAccelerated Technology EvolutionResource Sustainability ChallengeThe Value Realization GapThe Bento Box Approach: Rethinking POC ArchitectureBuilding with Bento Box: From Concept to CodeFrom Blueprint to Reality: Orchestrating AI ImplementationShifting Principles to Practice: Our Learnings from the FieldBuilding the FoundationInnovation in ActionLeveraging Talent: Building Teams and Frameworks That ScaleShow, Don’t Just Tell: The Demo That Covers “Wow” to “How”Making the Invisible VisibleSetting the StageChoreographing the StoryCrafting Your Demo Story (It’s Like a Real Function, You Know!)Key Learnings from This Chapter
4. Code to Scale – Engineering GenAI for Production
Architecting for the Big LeaguesTaming the Data BeastGuardrails and SecurityAnya’s NotesConcepts in PracticeFrom POC to Production – Scaling GenAI for EnterpriseThe Reality of Scale: When Success Creates New ChallengesFrom Foundation to ScaleThe Dynamics of Enterprise ScaleThe Continuous Adaptation ImperativeProgressive Scaling: Building on Solid FoundationsArchitecting for the Big LeaguesThe Foundation ShiftBreaking Free from MonolithsBuilding Enterprise-Grade FoundationsThe Art of Performance OptimizationBuilding for ReliabilityA Tale of Two ArchitecturesInitial Architecture: The Straightforward ApproachEnterprise Architecture: Designed for Scale and ReliabilityThe Entry Point: Load Management LayerThe API Layer: Gateway to ServicesThe Processing Layer: Intelligence at ScaleThe Storage Layer: Data Management at ScaleThe Monitoring Layer: Ensuring Reliable OperationHow It All Works TogetherBuilding with Governance in Mind: From Day One to Enterprise ScaleThe Three Pillars of Enterprise AI GovernanceRegional Compliance: The Foundation’s FoundationData GovernanceSecurity: The Concentric Circles of ProtectionA Framework for Forward ThinkingKey Learnings from This Chapter
5. The Goblet of Governance - Habits of Security and Ethics
A Timely GuideGovernance at SpeedAI Governance Framework – Establishing the FoundationData Governance: The Backbone of AI GovernanceRisk Management FrameworkEthical AI ImplementationOperational GovernanceThe Security Investment PyramidAnya’s NotesConcepts in PracticeGuardrails: Building Habits for Governance, Security, and EthicsReframing the AI LandscapeLayered Security – The AI Security OnionGovernance and PoliciesInfrastructureApplicationDataPractical Guardrails in ActionCompliance Discovery CanvasIntegrated Risk Management and Policy FrameworksThe Economics of Security – Investment for ResilienceOperationalizing Governance – From Concept to Everyday PracticeKey Learnings from This Chapter

6. The Age of Agents - From Scripts to Sidekicks
Think Fast, Think Slow: The Art of AI AgencyThe Reality of Real-World Data: FinCorp’s StoryFinding Our Way: The Agent Assessment FrameworkAnya’s NotesConcepts in PracticeGenesis and Evolution of AI AgentsThe Timeline of LLMs and Their EngineeringThe Limits of “Vanilla” LLMsEnter Compound Systems: A Step Toward Agentic ThinkingWhat Is This Control Logic, by the WayProgrammatic Control Logic: Known Paths, Defined by HumansAgentic Control Logic: Self-Directed Decisions by the LLMComparing Assistance Control Logic (RAG) vs. Agentic Control LogicCore Components of Agentic Systems: ReAct Design principleThe ReAct Principle: Integrating Reasoning and ActingReasoning: Building a Chain-of-ThoughtActing: Engaging External ResourcesSynergy of Reasoning and ActingImplications and ApplicationsMixture of Experts: A Deeper Look into Efficient LLM ArchitectureWhy Reasoning Is ExpensiveThe Core Idea Behind MoELet’s See an Example of Such LLM: Mixtral 8x7BStrategic Implementation Framework for AI AgentsProblem-First vs. Technology-First ApproachMapping Workflow ReasoningIdentifying Reasoning NeedsIntegrating Human Oversight and Risk ManagementBest Practices for Embedding OversightMerging Agentic and Compound SystemsA New Horizon: Where AI Agents Go from HereEmerging Trends and Future ChallengesRemember the Demo Dr. Anya Gave on 1+1 and DeepSeek Going into a Deep Analysis?A Final Word from Dr. AnyaKey Learnings from This Chapter
Index

Content preview from The Value Vector: Building Scalable Generative AI-Based Applications

About the Authors

Jaydeep Chakrabarty

A person wearing glasses and a black Adidas hoodie is smiling against a dark background.

is a versatile technology leader with over two decades of experience across full-stack development, infrastructure, experience and visual design, project management, quality analysis, and security. He is passionate about open-source projects and has contributed to notable initiatives such as Talisman, OpenStack, Report-mine, Taiko, and EEG studies. He has served as the Head of AI and Generative AI for Thoughtworks in India and the Middle East, and he is currently the Director of AI in Tech at Piramal Finance. He regularly shares his insights through national newspapers, including The Hindu’s tech column, and ...

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The Value Vector: Building Scalable Generative AI-Based Applications

by Jaydeep Chakrabarty, Harinee Muralinath

About the Authors

Become an O’Reilly member and get unlimited access to this title plus top books and audiobooks from O’Reilly and nearly 200 top publishers, thousands of courses curated by job role, 150+ live events each month,
and much more.