What's inside the playbook
You'll learn:
Vector databases loaded last Tuesday can answer questions about last Tuesday, but customer-facing RAG needs retrieval that reflects who a person is right now. This playbook covers the architecture, retrieval stack, real-time sync patterns, privacy controls, and evaluation frameworks production teams are using to close that gap, with every recommendation grounded in enterprise deployments and current benchmarks.
- How to set SLAs for retrieval latency, data freshness, and accuracy before you pick tools
- Inventory and normalization patterns for live customer data across CRM, support, behavioral, and transactional sources
- Retrieval stack selection: when vector, keyword, or hybrid search fits, with current vector database benchmarks
- Change data capture and incremental indexing patterns that move source system updates to a retrievable index in seconds
- Structured context injection through the Model Context Protocol (MCP), with field-level redaction, schema validation, and audit logging
- Evaluation frameworks like RAGAS, plus the retrieval, generation, and business metrics that actually matter
- A phased rollout blueprint drawn from production deployments at DoorDash, LinkedIn, and enterprise banking
- Re-ranking and knowledge graph techniques for scaling accuracy on complex, multi-domain queries
Vector databases loaded last Tuesday can answer questions about last Tuesday, but customer-facing RAG needs retrieval that reflects who a person is right now. This playbook covers the architecture, retrieval stack, real-time sync patterns, privacy controls, and evaluation frameworks production teams are using to close that gap, with every recommendation grounded in enterprise deployments and current benchmarks.
