rag-implementation

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

Skills to Invoke

• ai-product - AI product design
• rag-engineer - RAG engineering

Actions

1. Define use case
2. Identify data sources
3. Set accuracy requirements
4. Determine latency targets
5. Plan evaluation metrics

Copy-Paste Prompts

Use @ai-product to define RAG application requirements

Phase 2: Embedding Selection

Skills to Invoke

• embedding-strategies - Embedding selection
• rag-engineer - RAG patterns

Actions

1. Evaluate embedding models
2. Test domain relevance
3. Measure embedding quality
4. Consider cost/latency
5. Select model

Copy-Paste Prompts

Use @embedding-strategies to select optimal embedding model

Phase 3: Vector Database Setup

Skills to Invoke

• vector-database-engineer - Vector DB
• similarity-search-patterns - Similarity search

Actions

1. Choose vector da