RAG-only architectures
What is RAG-only architectures?
RAG-only architectures are systems built entirely around retrieval-augmented generation, where AI models answer questions by first searching through external documents or databases rather than relying solely on their training data.
As enterprises deploy RAG systems for production use, understanding how to architect, secure, and optimize them end-to-end is becoming a critical practical skill rather than just a theoretical concept.
References
- RAG From Zero: The Mental Model I Wish I Had as a Beginner — Medium: Large Language Models
- Vector Databases Explained Visually: Which One Should You Use? — YouTube
- RAG: Giving LLMs Access to External Knowledge — Medium: Large Language Models
- RAG Evaluation in Practice — Faithfulness, Answer Relevance, and Context Precision in RAGAS — Medium: Large Language Models
- What Happens When Your RAG System Retrieves the Wrong Documents? — Hacker News
- RAG Fundamentals: Embeddings, Chunk Size, Chunk Overlap, and Retrieval Quality — Medium: Large Language Models
- RAG Just Became a Trainable Neural Graph (WikiFM) — YouTube
- Building an Integrated Vector Database System in PostgreSQL — Hacker News