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Build a Production-Ready RAG System with Python & OpenAI
6 students
What you'll learn
- Understand how text embeddings convert human language into numerical vectors that capture semantic meaning, enabling similarity-based search.
- Describe the complete RAG pipeline, including the five key stages.
- Explain what Retrieval-Augmented Generation (RAG) is and why it is often superior to fine-tuning for document-based question-answering applications.
- Set up a professional Python development environment with virtual environments to isolate project dependencies.
- Create and manage a requirements.txt file to document and install project dependencies efficiently.
- Securely manage sensitive credentials like API keys using environment variables and Streamlit’s secrets management system.
- Read and extract text content from various document formats such as PDF and TXT.
- Chunk large documents into smaller segments suitable for retrieval.
- Generate embeddings using the OpenAI API for semantic search.
- Store and index embeddings efficiently using a vector database.
- Execute similarity searches to retrieve relevant document chunks.
- Build core RAG logic that connects retrieval and generation into a working pipeline.
- Create an interactive Streamlit application for document chat functionality.
- Upload documents and ask questions that return grounded and cited answers.
- Test the RAG application using real-world documents.
- Deploy a working RAG system to Streamlit Cloud for public access.
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Course content
5 sections • 31 lectures • 1h 59m total length
Requirements
- Basic computer literacy (file navigation, copy/paste, typing).
- A computer running Windows, macOS, or Linux.
- Internet access for using the OpenAI API and deployment tools.
- A free OpenAI account to obtain an API key.
- Basic programming concepts are beneficial but not mandatory.
Description
Build your own fully working AI system that can read your documents and answer questions with accuracy.
In this step-by-step, project-based course, you will learn how to use Retrieval-Augmented Generation (RAG) to overcome the limitations of traditional AI models. Instead of relying solely on the model’s internal memory, you will connect GPT to your own knowledge sources such as PDFs, policies, reports, and business documentation.
You will learn the complete pipeline: document ingestion, chunking, embeddings, vector search, and contextual answer generation. All of this will be combined into a clean, user-friendly Streamlit application that you can run locally or deploy to the cloud.
Throughout the course, you will gain hands-on skills in Python, the OpenAI API, semantic search, creating embeddings, designing a chat interface, and deploying applications online.
By the end of the course, you will have built and shipped a working RAG system that you can personalize, extend, and showcase in your portfolio. Whether your goal is automating customer support, improving document access, or creating new AI-powered products, this project provides a strong foundation for building real-world AI solutions.
This course is accessible to beginners, while still offering depth for intermediate learners who want to advance their AI engineering skills.
Who this course is for:
- Learners who want to build practical AI applications from scratch.
- Business professionals who want to automate knowledge access using AI.
- Developers seeking hands-on experience with Retrieval-Augmented Generation (RAG).
- Tech students wanting project-based portfolio content.
- IT consultants and freelancers delivering AI solutions to clients.
- Small business owners wanting smarter internal search tools.
- Anyone curious about how to use AI with their own documents.