RAG 101: Retrieval-Augmented Generation End to End
Build a reliable knowledge assistant with retrieval, reranking, and grounded responses.
RAG 101: Retrieval-Augmented Generation End to End If you want an AI system that answers with fresh, accurate knowledge, you need retrieval. RAG (Retrieval-Augmented Generation) is the standard pattern: retrieve relevant information, then generate a grounded response. This tutorial walks you through the full pipeline and shows the decisions that matter. The Core Idea A large language model stores knowledge in its parameters, but it does not know your private documents. RAG adds a retrieval step so the model can cite or summarize information from your data. The workflow is simple: **Ingest** documents **Chunk** them **Embed** chunks into vectors **Index** them in a vector store **Retrieve** relevant chunks for a query **Generate** a response using those chunks Step 1: Collect and Clean Your Data Start with the data you want the model to answer from: docs, FAQs, internal guides, or policies. Clean it: Remove duplicated paragraphs Strip navigation text Keep the content meaningful on its own If the data is messy, the model will be messy too. Step 2: Chunk the Content Long documents must be split. Chunk size is a tradeoff: Small chunks = precise retrieval but less context Large chunks = more context but less precision A strong starting point is to chunk by headings and keep overlap between chunks. Step 3: Create Embeddings Embeddings turn text into vectors so you can search by meaning instead of exact words. Each chunk becomes a vector in your index. You do not need to understand the math to use embeddings, but you should know this: the model can only retrieve what you embed. Step 4: Index and Search Store vectors in a vector database or search engine. At query time: Embed...
Tags: Generative AI, RAG, Search