Rag Implementation

---
name: rag-implementation
description: RAG (Retrieval-Augmented Generation) implementation workflow covering embedding selection, vector database setup, chunking strategies, and retrieval optimization.
---

# 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
- `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
```
```

### 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

#### Actions
1. Choose vector database
2. Design schema
3. Configure indexes
4. Set up connection
5. Test queries

#### Copy-Paste Prompts
```
```

### 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

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

#### Copy-Paste Prompts
```
```

```
```

### Phase 6: LLM Integration

#### Skills to Invoke

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

#### Copy-Paste Prompts
```
```

### Phase 7: Caching

#### Skills to Invoke
- `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
```
```

### Phase 8: Evaluation

#### Skills to Invoke
- `llm-evaluation` - LLM 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-agent-development` - AI agents

## 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.