Understand and build a working retrieval pipeline so your LLM answers from your own documents, with citations.
AI & PromptsPDF · 15 pages· v1.0
4.3Retrieval-Augmented Generation (RAG) is how you get an LLM to answer questions about your own documents instead of making things up. This guide explains the whole pipeline in plain language and then builds a minimal working version you can run and understand end to end. It's for developers and technical builders who keep hearing 'just use RAG' but want to know what's actually happening: chunking, embeddings, vector search, and how the retrieved text gets stitched into the prompt. You need basic Python. You don't need a database degree or prior ML experience. The guide walks through each stage with annotated code: loading and chunking documents sensibly, turning chunks into embeddings, storing and searching them, retrieving the most relevant pieces for a question, and assembling a grounded prompt that asks the model to answer only from the provided context and cite its sources. Crucially, it also covers why RAG goes wrong — bad chunking, retrieving too little or too much, and the model ignoring the context — and how to diagnose each. After finishing, you'll be able to build a question-answering system over a folder of documents, explain every component to a colleague, and make informed choices about chunk size, retrieval count, and when a managed vector database is worth it. The example uses a tiny in-memory store so you can run it with no infrastructure, then points to how it scales. Delivered as a single Markdown file with runnable code.
Not to learn it. The example uses a tiny in-memory store so you can run everything with no setup. The guide explains when a managed vector database becomes worth it and what changes.
The concepts are universal. You need an embeddings endpoint and a chat endpoint from any provider; the guide marks where to plug in your SDK.
It greatly reduces it when done well, because the model answers from retrieved text. The guide shows the prompt pattern that tells the model to say 'I don't know' when the context lacks the answer.
The in-memory version is fine for hundreds to low thousands of chunks. Beyond that you move to a real vector store; the guide explains the transition.
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