RAG Architecture Explained: From Documents to Answers

This article details the architecture of a Retrieval-Augmented Generation (RAG) system, explaining its core components and their responsibilities. It outlines a pipeline that includes document parsing, chunking, embedding, vector storage, semantic search, and response generation, emphasizing the importance of separating these stages for independent testing and reliable operation. The author uses their internal knowledge assistant, Guidely, as an example to illustrate how RAG systems retrieve relevant information from documents to construct answers, complete with citations. AI

IMPACT Provides a blueprint for building and scaling RAG systems, crucial for developing reliable AI-powered knowledge assistants and applications.

RANK_REASON The articles describe the technical implementation and architecture of Retrieval-Augmented Generation (RAG) systems, which are tools for building AI applications.

Read on dev.to — LLM tag →

• aiohttp
• asyncio
• Faiss
• FastAPI
• gpt-4
• Mermaid
• NumPy
• OpenAI
• PlantUML
• text-embedding-ada-002
• Guidely
• React Frontend
• retrieval-augmented generation
• TrustLayer

• product
• infra

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