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 database 2. Design schema 3. Configure indexes 4. Set up connection 5. Test queries

Copy-Paste Prompts

Use @vector-database-engineer to set up vector database

Phase 4: Chunking Strategy

Skills to Invoke

• rag-engineer - Chunking strategies
• rag-implementation - RAG implementation

Actions

1. Choose chunk size 2. Implement chunking 3. Add overlap handling 4. Create metadata 5. Test retrieval quality

Copy-Paste Prompts

Use @rag-engineer to implement chunking strategy

Phase 5: Retrieval Implementation

Skills to Invoke

• similarity-search-patterns - Similarity search
• hybrid-search-implementation - Hybrid search

Actions

1. Implement vector search 2. Add keyword search 3. Configure hybrid search 4. Set up reranking 5. Optimize latency

Copy-Paste Prompts

Use @similarity-search-patterns to implement retrieval

Use @hybrid-search-implementation to add hybrid search

Phase 6: LLM Integration

Skills to Invoke

• llm-application-dev-ai-assistant - LLM integration
• llm-application-dev-prompt-optimize - Prompt optimization

Actions

1. Select LLM provider 2. Design prompt template 3. Implement context injection 4. Add citation handling 5. Test generation quality

Copy-Paste Prompts

Use @llm-application-dev-ai-assistant to integrate LLM

Phase 7: Caching

Skills to Invoke

• prompt-caching - Prompt caching
• rag-engineer - RAG optimization

Actions

1. Implement response caching 2. Set up embedding cache 3. Configure TTL 4. Add cache invalidation 5. Monitor hit rates

Copy-Paste Prompts

Use @prompt-caching to implement RAG caching

Phase 8: Evaluation

Skills to Invoke

• llm-evaluation - LLM evaluation
• evaluation - AI evaluation

Actions

1. Define evaluation metrics 2. Create test dataset 3. Measure retrieval accuracy 4. Evaluate generation quality 5. Iterate on improvements

Copy-Paste Prompts

Use @llm-evaluation to evaluate RAG system

RAG Architecture

User Query -> Embedding -> Vector Search -> Retrieved Docs -> LLM -> Response
                |              |              |              |
            Model         Vector DB     Chunk Store    Prompt + Context

Quality Gates

• [ ] Embedding model selected
• [ ] Vector DB configured
• [ ] Chunking implemented
• [ ] Retrieval working
• [ ] LLM integrated
• [ ] Evaluation passing

Related Workflow Bundles

• ai-ml - AI/ML development
• ai-agent-development - AI agents
• database - Vector databases

Limitations

• Use this skill only when the task clearly matches the scope described above.
• Do not treat the output as a substitute for environment-specific validation, testing, or expert review.
• Stop and ask for clarification if required inputs, permissions, safety boundaries, or success criteria are missing.