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

• Define use case

• Identify data sources

• Set accuracy requirements

• Determine latency targets

• 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

• Evaluate embedding models

• Test domain relevance

• Measure embedding quality

• Consider cost/latency

• 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

• Choose vector database

• Design schema

• Configure indexes

• Set up connection

• 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

• Choose chunk size

• Implement chunking

• Add overlap handling

• Create metadata

• 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

• Implement vector search

• Add keyword search

• Configure hybrid search

• Set up reranking

• 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

• Select LLM provider

• Design prompt template

• Implement context injection

• Add citation handling

• 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

• Implement response caching

• Set up embedding cache

• Configure TTL

• Add cache invalidation

• 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

• Define evaluation metrics

• Create test dataset

• Measure retrieval accuracy

• Evaluate generation quality

• 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.