# rag-engineer

> Expert in building Retrieval-Augmented Generation systems. Masters embedding models, vector databases, chunking strategies, and retrieval optimization for LLM applications. Use when: building RAG, vector search, embeddings, semantic search, document retrieval.

- Skill: `dokhacgiakhoa/rag-engineer` (Agent Skill, multi-file: 7 files)
- Install (CLI): `npx skillmds add dokhacgiakhoa/rag-engineer`
- Raw SKILL.md: https://api.skillmd.com/api/skills/dokhacgiakhoa/rag-engineer/raw
- Safety review: pending (external: skill-scanner PASS, skillspector PASS)
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: AI & ML
- Author: Dokhacgiakhoa (https://skillmd.com/u/dokhacgiakhoa)
- Updated: 2026-08-19
- Page: https://skillmd.com/skills/dokhacgiakhoa/rag-engineer

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# 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

## 🧠 Knowledge Modules (Fractal Skills)

### 1. [Semantic Chunking](./sub-skills/semantic-chunking.md)
### 2. [Hierarchical Retrieval](./sub-skills/hierarchical-retrieval.md)
### 3. [Hybrid Search](./sub-skills/hybrid-search.md)
### 4. [❌ Fixed Chunk Size](./sub-skills/fixed-chunk-size.md)
### 5. [❌ Embedding Everything](./sub-skills/embedding-everything.md)
### 6. [❌ Ignoring Evaluation](./sub-skills/ignoring-evaluation.md)