rag-implementation
Complete workflow for building RAG systems from embedding selection through evaluation and optimization.
- Covers eight sequential phases: requirements analysis, embedding selection, vector database setup, chunking strategy, retrieval implementation, LLM integration, caching, and evaluation
- Includes actionable steps for each phase with specific skills to invoke and copy-paste prompts for agent commands
- Addresses core RAG concerns: embedding quality, vector indexing, chunk overlap handling, hybrid search configuration, prompt caching, and retrieval accuracy metrics
- Designed for semantic search, document Q&A, and knowledge-grounded AI applications with defined latency and accuracy targets
RAG Implementation Workflow
Overview
Specialized workflow for implementing RAG (Retrieval-Augmented Generation) systems including embedding model selection, vector database setup, chunking strategies, retrieval optimization, and evaluation.
When to Use This Workflow
Use this workflow when:
- Building RAG-powered applications
- Implementing semantic search
- Creating knowledge-grounded AI
- Setting up document Q&A systems
- Optimizing retrieval quality
Workflow Phases
Phase 1: Requirements Analysis
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