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LLM Apps in Production (RAG + Vector DB + Caching) · Lesson

Basic RAG System Architecture Overview

Understand the high-level components of a RAG system, including data sources, retrievers, and generators.

Basic RAG System Architecture Overview is a free LLM Apps in Production (RAG + Vector DB + Caching) lesson on CoddyKit — lesson 3 of 4. You can read the complete lesson below for free — then practise it hands-on in the browser with a built-in code editor and a 24/7 AI tutor. It is part of the LLM Apps in Production (RAG + Vector DB + Caching) learning path, one of 4 lessons in the course, and your progress syncs across the web and the CoddyKit app.

Intro to RAG Architecture

Now let's look at the building blocks of a RAG system. Knowing the architecture shows you exactly how RAG makes LLMs more accurate and reliable.

The Three Main Pillars

RAG blends retrieval (finding info) and generation (creating text) through three components: data sources, a retriever, and a generator.

Component 1: Data Sources

The data sources hold the external knowledge your LLM wasn't trained on — PDFs, web pages, internal wikis, database records, and more.

Preparing Your Data

First your data gets processed: large documents are split into smaller, searchable pieces called chunks, like indexing a library by chapter instead of by whole book.

Component 2: The Retriever

The retriever is a smart search engine for your chunks. It understands the meaning of a query, not just keywords, to fetch the most relevant context.

Retriever in Action

When a user asks, the retriever scans all processed chunks and pulls out the few most likely to hold the answer, passing them along as context.

Component 3: The Generator (LLM)

The generator is your LLM — the part that writes the response. In RAG it gets two inputs: your original question and the context the retriever found.

LLM's Role: Grounded Responses

Instead of leaning on pre-trained knowledge alone, the LLM uses retrieved context to ground its answer, like an expert handed the exact reference documents.

The Full RAG Flow

The full RAG flow: query in, retriever finds chunks, query plus chunks go to the generator, the LLM writes a grounded answer, the user gets a factual reply.

Check Your Understanding

Which component is primarily responsible for finding relevant information from your knowledge base when a user asks a question?

RAG Architecture Recap

Recap: a RAG system has three parts — data sources (the knowledge), retriever (the smart search), and generator (the LLM crafting grounded answers).

Frequently asked questions

Is the “Basic RAG System Architecture Overview” lesson free?

Yes — the full text of “Basic RAG System Architecture Overview” is free to read here on the web, and the LLM Apps in Production (RAG + Vector DB + Caching) course includes 4 lessons in total. To practise it interactively (a built-in code editor and a 24/7 AI tutor) and unlock the rest of the LLM Apps in Production (RAG + Vector DB + Caching) course, upgrade to CoddyKit PRO.

What will I learn in “Basic RAG System Architecture Overview”?

Understand the high-level components of a RAG system, including data sources, retrievers, and generators. You practise LLM Apps in Production (RAG + Vector DB + Caching) with hands-on code you run directly in the browser, and a 24/7 AI tutor answers your questions as you work through the lesson.

Do I need any experience to start LLM Apps in Production (RAG + Vector DB + Caching)?

No prior experience is required. LLM Apps in Production (RAG + Vector DB + Caching) on CoddyKit is structured for beginners through advanced learners; this is — lesson 3 of 4, so you can start here or from the beginning and move at your own pace.

How long does the “Basic RAG System Architecture Overview” lesson take?

Most CoddyKit lessons take about 5–10 minutes. Each one is bite-sized and interactive, so you make steady progress and pick up exactly where you left off across the web and the app.

Can I write and run code in this LLM Apps in Production (RAG + Vector DB + Caching) lesson?

Yes. Every LLM Apps in Production (RAG + Vector DB + Caching) lesson includes a built-in code editor, so you write and run real code right in your browser and get instant AI feedback — no local setup required.

All lessons in this course

  1. Understanding LLM Apps in Production
  2. Fundamentals of Retrieval Augmented Generation
  3. Basic RAG System Architecture Overview
  4. Prompt Engineering & Context Windows
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