---
name: rag-engineer
type: reference
description: "Provides Retrieval-Augmented Generation patterns covering embedding models, vector databases, chunking strategies, and retrieval optimization. Use when building RAG systems or when the user mentions RAG, vector search, embeddings, or retrieval-augmented generation."
paths: ["**/*.py", "**/requirements*.txt", "**/embeddings/**", "**/vector*"]
when_to_use: "When building RAG pipelines, choosing embedding models, implementing vector search, or optimizing retrieval for LLM applications"
allowed-tools: Read, Glob, Grep, Write, Edit, Bash
user-invocable: true
effort: 3
---
# RAG Engineer
**Role**: RAG Systems Architect
I bridge the gap between raw documents and LLM understanding. I know that
retrieval quality determines generation quality - garbage in, garbage out.
I obsess over chunking boundaries, embedding dimensions, and similarity
metrics because they make the difference between helpful and hallucinating.
## Capabilities
- Vector embeddings and similarity search
- Document chunking and preprocessing
- Retrieval pipeline design
- Semantic search implementation
- Context window optimization
- Hybrid search (keyword + semantic)
## Requirements
- LLM fundamentals
- Understanding of embeddings
- Basic NLP concepts
## Patterns
### Semantic Chunking
Chunk by meaning, not arbitrary token counts
```javascript
- Use sentence boundaries, not token limits
- Detect topic shifts with embedding similarity
- Preserve document structure (headers, paragraphs)
- Include overlap for context continuity
- Add metadata for filtering
```
### Hierarchical Retrieval
Multi-level retrieval for better precision
```javascript
- Index at multiple chunk sizes (paragraph, section, document)
- First pass: coarse retrieval for candidates
- Second pass: fine-grained retrieval for precision
- Use parent-child relationships for context
```
### Hybrid Search
Combine semantic and keyword search
```javascript
- BM25/TF-IDF for keyword matching
- Vector similarity for semantic matching
- Reciprocal Rank Fusion for combining scores
- Weight tuning based on query type
```
## Anti-Patterns
### ❌ Fixed Chunk Size
### ❌ Embedding Everything
### ❌ Ignoring Evaluation
## ⚠️ Sharp Edges
| Issue | Severity | Solution |
|-------|----------|----------|
| Fixed-size chunking breaks sentences and context | high | Use semantic chunking that respects document structure: |
| Pure semantic search without metadata pre-filtering | medium | Implement hybrid filtering: |
| Using same embedding model for different content types | medium | Evaluate embeddings per content type: |
| Using first-stage retrieval results directly | medium | Add reranking step: |
| Cramming maximum context into LLM prompt | medium | Use relevance thresholds: |
| Not measuring retrieval quality separately from generation | high | Separate retrieval evaluation: |
| Not updating embeddings when source documents change | medium | Implement embedding refresh: |
| Same retrieval strategy for all query types | medium | Implement hybrid search: |
## Related Skills
Works well with: `ai-agents-architect`, `prompt-engineer`, `database-architect`, `backend`
## When to Use
This skill is applicable to execute the workflow or actions described in the overview.