book

RAG-Ready Patterns for Data Platforms

by Ravi Vedula, Gerardo Bodegas Martinez, Maruti Chittajallu, Jack Pullikottil

November 2026

Intermediate

362 pages

10h 50m

English

O'Reilly Media, Inc.

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Preface
Why We Wrote This BookWho Should Read This BookWhat This Book Is and Is NotNavigating This BookConventions Used in This BookUsing Code ExamplesO’Reilly Online LearningHow to Contact UsAcknowledgments
1. The Pillars of RAG Readiness
Why the Gap ExistsWhy RAG-Ready Patterns Are Needed NowA Practical RAG-Readiness FrameworkPillar 1: Data AssetsPillar 2: InfrastructurePillar 3: Trust LayerQuick Self-Assessment Across All PillarsConclusion
2. The Semantic Backbone: Designing Data Assets for RAG
Identify the Entity KernelCapture Source Semantics in Data ContractsGovern Semantic ChangeOrganize Assets into Layered Semantic Asset TaxonomySource-Boundary AssetsEntity-Kernel AssetsActivity-Grain AssetsDimensionsMetricsSegmentsDerived Serving ArtifactsUse Portable, Declarative DefinitionsMake Metadata Available Through Suitable APIsPutting the Patterns TogetherConclusion
3. Metadata-First Pipeline Engineering
Semantic Definitions Aren’t EnoughThe Enforcement GapWhen Data LiesThe Metadata-First MindsetThe Pipeline ContractSchema and Semantic DefinitionsOperational GuaranteesGovernanceLineagePipelines-As-Code ModelWhy Code WinsDeveloper ExperiencePattern ExampleManaged Fabric Code PatternManaged Data Fabric ResponsibilitiesBuilding a Pipeline ExampleLive Metadata for RAGSmarter RetrievalSelf-Healing ContextConclusion
4. Data Quality As a Retrieval Signal
Beyond the Enforcement GapWhen Plausible Data Misleads RAGWhy Intent Is Not EnoughActivating the ContractDefining the Quality SignalRetrieval PoliciesThe Quality EngineGenerating the SignalMonitoring As CodeContinuous Validation and GatekeepingThe Quality Signal As a ProductThe Quality Signal ProtocolStandardizing TrustThe Schema of TrustExposing State, Not ExplanationsDesign Constraints for Enterprise ScalePattern ExampleSignal-Aware RetrievalCompositional TrustSignal-Driven Prompt EngineeringThe Trust-Relevance Trade-OffTrust-Aware Orchestration in PracticeEstablishing a Culture of AccountabilityFrom Implicit to Visible TrustFrom Ownership to ActionThe Virtuous Cycle of Trust-Aware SystemsConclusion
5. Curated Grounding: Golden Answers and the Living Glossary
Why Clean Data Alone Doesn’t Answer Business QuestionsChallenges Specific to LLMs on Enterprise DataData Asset SemanticsData Asset Ownership and ManagementA Strategy Built on Curated GroundingGolden AnswersThe Golden Answer Validation WorkflowSchema, Metadata, and VersioningEvaluation and MonitoringThe Living GlossaryWhy a Glossary Matters for RetrievalGovernance and MaintenanceSchemaAccelerating Curation with AI: Scaling Human-in-the-Loop ReviewCandidate AuthoringUsing LLMs As GradersLinking, Routing, and Cross-Step MaintenanceProduction Monitoring and Backlog FeedbackConclusion
6. Scenario-Based Access Control for RAG Systems
The Access Control ParadoxContext and Least PrivilegeWhen Citation Becomes ExposureThe Governance Enforcement GapFrom “Who” to “Why”: The Scenario-Based Access PatternDefining the Access ScenarioApproved ScenariosScenario-Based Retrieval ScopeScenarios As the Governance FilterScenario EligibilityThe Access Scenario SpecificationThe Governance HandshakeCore FieldsThe Specification in PracticeScenarios and Privacy TagsEnforcing Scenarios: The Secure Retrieval Filter PatternThe Governance FilterCompositional Governance and the Intersection RuleDynamic Redaction and Policy EnforcementPattern Example: The Secure Retrieval FilterGoverning AI ResponsesIntent-Aware RelevanceGraceful Denial and User GuidanceAuditability and the Reason for the ResponseConclusion
7. Building RAG-Ready Data Products
Beyond the Gold Layer and the Data MeshWhere Data Mesh Raises a Different Problem“Isn’t This Just Data Mesh?”What a RAG-Ready Data Product Actually IsThe Product Boundary QuestionThe Running Example: Commercial Product UsageServing Every Modality: From Natural Language to CodeMultidepth: Same Product, Different PersonasStructured: The Primary ModalityMetadata: The Definitional ModalityUnstructured and Computed ModalitiesVersioned Contracts for Trust: The Federated Data Product FacadeThe Federated Data Product Facade ArchitectureThe Facade SpecificationThe Facade As Orchestration BackendVersioned Structure, Live SignalsBuilding the Facade IncrementallySupplying Business Meaning to the Facade: The SME as CoderDeclarative Definitions As Product MetadataThe SME Authoring ExperienceThe Grounding Portal As the BridgeDeclarative Definitions As Retrieval TargetsHow RAG-Ready Data Products Are Discovered, Selected, and MaintainedThe Discovery Problem RevisitedTopic Taxonomy: Organizing Discovery Around Business QuestionsHow Selection Actually WorksThe Data Product Manager As the Human FacadeThe Data Product LifecycleConclusion
8. Orchestrating RAG with the Model Context Protocol
The Universal Connector: An Introduction to MCPWhy MCP Is Gaining AdoptionPitfalls to Be Aware OfExposing Data Products As MCP ServicesControl and TrustDiscovery, Provenance, and SemanticsData OperationsPlatform OperationsBuilding Transparent RAG Chains with Human-in-the-Loop ParticipationExample 1Example 2Example 3Example 4Example 5Example 6Guidelines and Recommendations for MCP RAG ChainsDesign MCP Tools with a Security-First PostureObservability As a Core Design GoalImpose Discipline to Prevent Tool SprawlGovern Workflows, Not Just ToolsIsolate MCP Integrations from Core Business LogicA Playbook for Agentification with MCPBaselineWhere to Start and When to AdvanceFrom Observability to the Platform’s Nervous SystemObservability from MCP ToolsKey Metrics Sourced from MCP UsageFrom Signals to ImprovementsConclusion
9. Designing the Enterprise RAG Stack
From Prototype to Production: The Enterprise ImperativeThe Ten ChallengesLessons LearnedThe Enterprise RAG Lifecycle: A Reference ArchitectureHow the Lifecycle Builds on Earlier ChaptersWhere MCP Tools Slot InThe IDEAS Copilot v2 as an Instance of the LifecycleThe Enterprise RAG Capability ManifestThe Full Stack: Control Plane, Serving Plane, and Feedback LoopThe Query Understanding CapabilityIntent Classification: From Bespoke Routing to Grounding-Driven DisambiguationThe Question-to-Asset OntologyEntity Resolution on a Data PlatformTemporal GroundingThe Adaptive Retrieval CapabilityMultimodal Retrieval over a Data PlatformGrounding-Driven Query GenerationQuality-Gated RetrievalThe Response Assurance CapabilityCitation-First Response GenerationGuardrails and Graceful DenialThe Feedback-Driven Improvement LoopDesigning for Modularity and SafetyLineage-Aware Retrieval and Data VersioningSchema-Driven Grounding and Query GenerationMetric Certification Trust ChainsGovernance-Integrated SynthesisLatency and Cost BudgetsEvals As an Operating DisciplineThe Living ArchitectureConclusion

10. RAG-Readiness Scorecards
What the Scorecards DiagnoseA Common Assessment DisciplineFacade Completeness IndexHow the FCI WorksWhat the FCI Result RevealsReading the Portfolio, Not Just the PercentageDefinition Governance IndexHow the DGI WorksChoosing and Maintaining the Definition InventoryGrounding Coverage MatrixHow the GCM WorksWhat the GCM Result RevealsCoverage, Depth, and Validation Are Separate QuestionsStaleness Detection FrameworkHow the SDF WorksApplying the Lowest-Wins Rule in PracticeConsequences by Artifact ClassWhat the SDF Result RevealsRAG Failure Diagnostic TreeHow the RFDT WorksBranch 1: Wrong AnswerBranch 2: No AnswerBranch 3: Slow AnswerBranch 4: Inconsistent AnswerBranch 5: Unauthorized or Noncompliant InformationWhat a Validation Record Must ContainCoverage Does Not Equal EffectivenessFeedback Conversion RateHow the FCR WorksWhat the FCR Result RevealsThe Composite RAG-Readiness ProfileEstablishing the Assessment FrameNormalizing Without Erasing MeaningReading the ShapeCompact AnalyticsCo IllustrationThe Profile As Shared Diagnostic LanguageFrom Profile to PlanReading the Profile As a Set of FindingsWhat the Profile Orders and What It Does NotRouting Findings to Established PatternsWhen the Profile ExpiresConclusion
11. Practices and Pitfalls: Building the Future of RAG-Ready Enterprises
Practices That Always Pay OffPractice 1: Start Narrow, Expand Through MetadataPractice 2: Make Metadata the Medium of CollaborationPractice 3: Emit Machine-Readable Trust SignalsPractice 4: Govern Purpose, Not People—Scenario-Based Access ControlPractice 5: Run Governance as InfrastructurePractice 6: Let Feedback Loops Refactor the PlatformPractice 7: Distribute Ownership, Centralize StandardsPitfalls That Derail TrustPitfall 1: The Ontology TrapPitfall 2: Code-First ChaosPitfall 3: Shadow GovernancePitfall 4: Hallucination by DesignPitfall 5: The Hero Team SyndromePitfall 6: Neglecting Human-in-the-Loop ReviewsPitfall 7: Measuring Vanity, Not ValueProving Value in IncrementsAnchor on Outcomes, Not PlatformsMeasure What Builds TrustTurn Success into HabitMake Progress Legible to LeadershipAvoid the Trap of Over-PromisingLooking AheadFrom Analytics Platforms to Conversational SubstratesThe Rise of Agentic RetrievalFederated Trust, Runtime GovernanceSustainable Intelligence at ScaleConclusion: From Readiness Gap to Readiness Mindset
Index
About the Authors

Content preview from RAG-Ready Patterns for Data Platforms

Index

A

ABAC (attribute-based access control), Pillar 3: Trust Layer
access controls (see also SBAC (scenario-based access control))
- attribute-based, Pillar 3: Trust Layer
- based on intent, Scenario-Based Access Control for RAG Systems
- paradox of, The Access Control Paradox-The Governance Enforcement Gap
- role-based, Pillar 3: Trust Layer
access scenarios (see also SBAC (scenario-based access control))
- approved, Approved Scenarios
- defining, Defining the Access Scenario
- differing candidate pools from, Branch 4: Inconsistent Answer
- eligibility, Scenario Eligibility
- as governance filter, Scenarios As the Governance Filter-Scenarios As the Governance Filter
- governance of, Restricted scenario enrollment
- retrieval eligibility set by, The Adaptive Retrieval Capability
- retrieval scope of enrolled scenarios, Scenario-Based Retrieval Scope
- Secure Retrieval Filter Pattern, Enforcing Scenarios: The Secure Retrieval Filter Pattern-Pattern Example: The Secure Retrieval Filter
  - compositional governance and Intersection Rule, Compositional Governance and the Intersection Rule-Compositional Governance and the Intersection Rule
  - dynamic redaction and policy enforcement, Dynamic Redaction and Policy Enforcement
  - filter placement in retrieval pipeline, The Governance Filter
  - TypeScript implementation example, Pattern Example: The Secure Retrieval Filter-Pattern Example: The Secure Retrieval Filter
- specification, The Access Scenario Specification-Scenarios and Privacy Tags
  - core fields, Core Fields-lifecycle
  - “Customer Support Troubleshooting” ...

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RAG-Ready Patterns for Data Platforms

by Ravi Vedula, Gerardo Bodegas Martinez, Maruti Chittajallu, Jack Pullikottil

Index

A

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,
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