Designing Retrieval-Augmented Generation (RAG) Infrastructure

Introduction

Retrieval-Augmented Generation (RAG) is a critical pattern in modern AI platform engineering. It bridges the gap between static LLM training data and enterprise-specific, dynamic information.

Architecture of RAG

1. Document Ingestion: Extracting text from documents.
2. Chunking: Splitting text into meaningful segments.
3. Embedding: Converting text chunks into dense vector representations.
4. Vector Storage: Storing embeddings in a vector database.
5. Retrieval & Generation: Querying the database to provide context to the LLM.

Conclusion

Building scalable RAG infrastructure is fundamental to bringing generative AI into enterprise environments safely and reliably.