rag-architect

---
name: rag-architect
description: >
license: MIT
---

# RAG Architect

Design retrieval-augmented generation pipelines with the right tradeoffs at each layer.

## Workflow

1. **Choose chunking strategy** -- Match chunk method to document structure
2. **Select embedding model** -- Balance dimensions, speed, and domain fit
3. **Choose vector DB** -- Match scale, features, and deployment model
4. **Design retrieval** -- Dense, sparse, hybrid, or reranked
5. **Evaluate** -- Measure faithfulness, relevance, and answer quality

## Reading Guide

| Decision                                       | File                                                         |
| ---------------------------------------------- | ------------------------------------------------------------ |
| Chunking strategies + embedding models         | [chunking-and-embedding.md](./chunking-and-embedding.md)     |
| Retrieval strategies + vector DBs + evaluation | [retrieval-and-evaluation.md](./retrieval-and-evaluation.md) |

## Quick Decision Matrix

| Document type  | Chunking                  | Embedding        | Retrieval       |
| -------------- | ------------------------- | ---------------- | --------------- |
| Code           | Semantic (AST-aware)      | Code-specialized | Hybrid + rerank |
| Legal/medical  | Document-aware (sections) | Domain-specific  | Dense + rerank  |
| Chat logs      | Sentence                  | General-purpose  | Dense           |
| Technical docs | Recursive                 | General-purpose  | Hybrid          |
| Mixed/unknown  | Recursive (fallback)      | General-purpose  | Hybrid + rerank |

## What You Get

- A RAG pipeline architecture specifying chunking strategy, embedding model, vector store, and retrieval method for your document types
- Concrete configuration recommendations (chunk size, overlap, dimensions, top-k) with rationale for each tradeoff
- An evaluation plan using RAGAS or equivalent metrics to validate retrieval quality before and after tuning

## Rules

1. Start simple -- fixed-size chunks + dense retrieval is a valid baseline
2. Measure before optimizing -- run RAGAS evaluation before adding complexity
3. Chunk overlap matters -- 10-20% overlap prevents context loss at boundaries
4. Embedding dimensions are a tradeoff -- higher is not always better (cost, latency)
5. Hybrid retrieval (dense + sparse) almost always beats either alone