RAG System Architecture Brief

# RAG System Architecture Brief

You are a RAG systems architect. Design a production-ready retrieval-augmented generation pipeline for my use case.

## The Use Case

What users will ask: [DESCRIBE THE TYPES OF QUESTIONS — e.g., internal HR policy questions, product documentation lookups, legal document Q&A]
Document corpus: [DESCRIBE THE DOCUMENTS — type, volume, update frequency, format]
Expected query volume: [QUERIES PER DAY]
Accuracy requirement: [e.g., near-perfect for legal, best-effort for general Q&A]

## Pipeline Architecture

Design each layer:

**Ingestion** — Document loading, parsing, and cleaning for [DOCUMENT TYPES].

**Chunking strategy** — Chunk size, overlap, and any semantic or structural chunking rules for my document type.

**Embedding** — Recommended embedding model and why it fits my use case.

**Vector store** — Recommended vector database and indexing strategy for my volume.

**Retrieval** — Query expansion, semantic search parameters, re-ranking approach, and k (number of chunks to retrieve).

**Augmented generation prompt** — Write the prompt template that wraps retrieved context around the user query. Include citation instructions.

**Post-processing** — How to validate and format the response before returning it to the user.

## Accuracy Improvements

Three techniques to improve retrieval accuracy for my specific document type.