Week 4 | Build a RAG Application from Scratch

About this event

Have you ever asked ChatGPT questions about your own documents and realized it couldn't answer accurately?

That's because Large Language Models don't automatically know your organization's private knowledge, business documents, policies, or research papers.

This is exactly why companies build Retrieval-Augmented Generation (RAG) systems.

RAG is one of the most in-demand AI technologies today, enabling AI assistants to answer questions using your own documents instead of relying only on pre-trained knowledge.

In this live hands-on session, you'll build a complete RAG application from scratch and understand every step involved in creating enterprise-grade AI assistants.

You'll upload PDF documents, convert them into embeddings, store them in a vector database, retrieve relevant information, and generate accurate AI-powered responses.

This is one of the most practical AI engineering projects and a skill highly valued by companies building enterprise AI solutions.

💻 Live Project

Build Your Own AI Document Assistant

During this session, you'll create an intelligent AI assistant that can:

• Upload PDF documents
• Read and process document content
• Convert text into embeddings
• Store embeddings in a vector database
• Retrieve relevant information based on user questions
• Generate accurate responses using an LLM

By the end of the session, you'll have a working AI application capable of answering questions from your own documents.

📚 What You'll Learn

• What is Retrieval-Augmented Generation (RAG)?
• Why enterprises use RAG instead of relying only on ChatGPT
• Document Processing
• PDF Parsing
• Text Chunking Strategies
• Embeddings Explained
• Vector Databases
• Similarity Search
• Retrieval Pipelines
• Prompt Engineering with Retrieved Context
• Building an End-to-End RAG Workflow

🎯 Session Agenda

Why AI Needs RAG

Understand the limitations of standalone LLMs and why enterprises rely on Retrieval-Augmented Generation.

Understanding RAG Architecture

Learn how documents, embeddings, vector databases, retrieval, and LLMs work together.

PDF Processing

Extract and prepare document content for AI.

Chunking Strategies

Learn how to split documents into meaningful chunks for better retrieval accuracy.

Embeddings

Understand how AI converts text into numerical representations for semantic search.

Vector Databases

Discover how AI stores and retrieves millions of document embeddings efficiently.

Similarity Search

Learn how AI finds the most relevant information before generating answers.

Live Coding

Build a complete RAG application from scratch.

Industry Best Practices

Learn how enterprises improve retrieval quality, reduce hallucinations, and build scalable AI assistants.

Interactive Q&A

Get answers to your AI engineering questions and receive guidance for your learning journey.

👥 Who Should Attend?

🎓 College Students

🎓 Final Year Students

💼 Fresh Graduates

👨‍💻 Software Engineers

🐍 Python Developers

📊 Data Analysts

☁️ Cloud & DevOps Engineers

🤖 AI Enthusiasts

Anyone interested in building intelligent AI assistants using enterprise AI technologies.

🎁 Exclusive Participant Benefits

Every participant attending the live session will receive:

🏆 CareerByteCode Recognized Live Participant Certificate

• Downloadable Digital Certificate
• Unique Certificate Verification Link
• Add your verified certificate directly to the Licenses & Certifications section of your LinkedIn profile
• Showcase your AI Engineering learning journey to recruiters and hiring managers

💻 Access to Practice Code

Participants who wish to receive the complete project source code and learning resources should:

• Share their learning experience on LinkedIn
• Tag Jenefer Rexee George
• Tag CareerByteCode
• Tag at least 2–3 friends

Eligible participants will receive the complete practice code and supporting resources after the session.

🚀 Learn one of the most sought-after AI engineering skills by building a complete RAG application from scratch. Transform PDFs into an intelligent AI assistant and gain practical experience with embeddings, vector databases, retrieval pipelines, and LLMs used in real enterprise AI solutions.