# RAG Architecture

> Implement Retrieval-Augmented Generation for enhanced AI responses

- **Category**: Cloud
- **Subcategory**: gcp
- **Canonical URL**: https://designpattern.fyi/patterns/gcp_rag_architecture/

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## Description
The RAG (Retrieval-Augmented Generation) architecture pattern combines language models with document retrieval to provide accurate, context-aware responses. In GCP, this typically uses Vertex AI for embeddings and generation, Vertex AI Search for document indexing, and Vector Search for semantic similarity matching.


## Use Cases
Use when building question-answering systems, knowledge base assistants, or applications that need to reference specific documents.





## Trade-offs


### Advantages

- Reduces model hallucinations

- Provides source attribution

- Enables knowledge from custom data

- More accurate and contextual responses




### Considerations & Drawbacks

- Requires document preprocessing

- Additional infrastructure complexity

- Latency from retrieval operations







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**Reference**: [Original Source](https://cloud.google.com/architecture/genai-retrieval-augmented-generation)