Build a Simple RAG Pipeline From Scratch

This article introduces Retrieval-Augmented Generation (RAG) by building a simple, functional pipeline from scratch. It explains RAG as a method to enhance LLM responses by providing relevant text from external documents directly within the prompt. The process involves loading documents, chunking them, embedding these chunks into vectors, retrieving the most similar chunks to a user's question, and finally generating an answer using the retrieved context. The author emphasizes understanding each step's mechanics and limitations, using Python and local embeddings for clarity and cost-effectiveness. AI

IMPACT Provides a foundational understanding and practical implementation of RAG, enabling developers to build question-answering systems on custom data.

RANK_REASON The article describes a practical implementation of a technique (RAG) using specific tools and code, rather than announcing a new frontier model or significant industry shift.

Read on dev.to — LLM tag →

• deepseek/deepseek-v4-flash
• LLM
• OpenAI
• OpenRouter
• Python
• sentence-transformers/all-MiniLM-L6-v2

• product
• other

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →