Discovery & Data Assessment: Review data sources, documents, and knowledge repositories; identify high-value retrieval and automation use cases; define success metrics.
RAG Architecture & Prototype: Design vector database schema, ingestion pipelines, and retrieval workflows; build a proof-of-concept RAG assistant or automation module for validation.
Workflow Orchestration Design: Map end-to-end processes—how retrieval outputs feed into automated steps (e.g., notifications, report creation, API calls); select orchestration framework or custom engine.
Implementation & Integration: Set up vector DB (Pinecone/Qdrant/Weaviate), ingestion pipelines, LLM prompts; develop automation scripts or services to consume retrieval results and perform actions; integrate with existing systems via APIs or connectors.
Testing & Validation: Conduct retrieval accuracy tests, end-to-end workflow simulations, and security assessments; refine embeddings, prompts, and orchestration logic based on feedback.
Deployment & Monitoring: Deploy components (ingestion, retrieval service, orchestration engine) in production; configure CI/CD pipelines, monitoring dashboards, and alerting for performance, errors, and data drift.
Iteration & Maintenance: Analyze usage and retrieval metrics, tune vector indexes, update prompts, and extend workflows with new RAG-driven automations according to evolving business needs.

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