RAG Tutorial with Code: Ship Your Own AI Assistant This Weekend

The exact blueprint for building a RAG chatbot that will save you dozens of hours

Two weeks ago, I shipped my first AI product.

My AI assistant has answered ~125 questions about data science and AI careers, pulling from my 50+ blog posts to give personalized advice. People are using it for real career guidance, and the feedback has been incredible:

“It felt just like my big bro with 10+ years of experience, helping me through situations and answering all the questions I've been stuck with… it's a blessing to have something like that when you're figuring things out all alone” - Aaditya S.

“I have already started using your AI assistant for project ideas and interview prep, it's super helpful!” - Tanmay H.

Here's what hit me: There's a massive gap between "I understand RAG conceptually" and "I built something people genuinely love."

Most tutorials show toy examples with perfect data. The reality is messier:

• Users type "ML advice" and expect magic

• My content lives across platforms in messy formats

• The interface needs to feel instant or people bounce

This tutorial gives you my exact blueprint. The dozens of hours I spent figuring out RAG systems, vector databases, API integrations, and frontend configuration. You get it all documented with code snippets.

How It All Works Together

Before diving into code, let's understand the complete flow:

Illustration of RAG implementation steps (image by author)

Building a RAG app is like creating a smart librarian for your content:

Step 1: Environment Setup

Getting your development environment ready with dependencies, API keys, and project structure. Think of this as setting up the librarian's workspace with all the necessary tools.

Step 2: Database Schema Setup

Setting up your database and search functions. This is like designing the library's filing system, i.e. where books go, how they're categorized, and how to find them quickly.

Step 3: Content Ingestion Pipeline

Fetching your blog posts and articles from various sources (Substack RSS, text files), cleaning, and processing them into searchable documents with embeddings. Think of this as gathering all your books, cataloging them, and creating detailed index cards for each section.

Step 4: Query Processing & Response Generation

When a user asks a question, we find the most relevant content pieces and use them as context for an LLM to generate natural responses. It's like the librarian instantly knowing which books contain information about what the user is asking, then reading the relevant sections and giving a personalized answer.

Step 5: Frontend Experience

All of this happens behind a clean Streamlit interface that streams responses in real-time and shows sources with citations. The user just types a question and gets an intelligent answer with references.

Step 6: Deploy to Production

Getting your assistant live with proper deployment, query logging, and production-ready configuration. This is like opening the library to the public with all systems running smoothly.

Why This Guide?

Most tutorials show toy examples with perfect data. This handles real messy content across platforms and teaches you the architecture decisions behind a production RAG system.

You get:

• Complete working RAG system from database setup to deployment

• Query logging to track usage patterns

• Real-world implementation that handles Substack HTML and archived posts as .txt files.

What you'll build: Live demo → An AI assistant with streaming responses and source citations.

Who this is for: Developers frustrated by RAG tutorials that break with real-world content.

Prerequisites: Basic Python, command line comfort

Running costs: typically under $10/month (depends on traffic and content volume)

While others spend weeks stuck in tutorial hell, you could ship your own AI assistant in a weekend.

Note: Some debugging and customization may be needed for your specific setup.

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The complete implementation includes: RSS content ingestion that handles HTML cleaning, smart chunking that preserves article context, production error handling, Streamlit deployment, and query logging. [Upgrade to read the full walkthrough →]

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