AI RAG Engineering

Transform your approach to AI-driven applications. Learn to build production-ready Retrieval-Augmented Generation (RAG) systems that intelligently combine large language models with contextual knowledge. Through hands-on course, you'll earn critical knowledge and develop a functional Chatbot.

Intermediate

Artificial Intelligence

2 days

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What you will learn in this course

WHY THIS COURSE IS A GAMECHANGER: Most engineers treat RAG as a black box – they may understand the theory but struggle to architect, optimize, and deploy RAG systems that work reliably in production. This course transforms RAG from abstract concepts into tangible, deployable skills. You'll build a complete chatbot system from scratch, understanding every component of the RAG pipeline.

ABOUT THIS COURSE: Instead of abstract examples, you're building a Chatbot – a real RAG system that answers customer questions by retrieving and synthesizing information from actual website content. By the end of the course, you'll have a working prototype that you can deploy. You'll learn abbout data ingestion, embeddings & vector search, LLM integration, security & quality controls, and Docker deployment.

Agenda

RAG Fundamentals & Architecture: Understanding RAG systems, real-world use cases, and architecture overview including data pipeline, retrieval, and generation.

Data Foundation & Embeddings: Web scraping, document chunking, preprocessing, embedding models, vector databases, and building a queryable vector store.

Advanced Retrieval: Hybrid search combining vector similarity and keyword search, reranking, Top-K optimization, and benchmarking retrieval performance.

LLM Integration & Chat Backend: Choosing LLMs, prompt engineering, building FastAPI application with chat endpoints, streaming responses, and session management.

Security & Production Readiness: Input validation, prompt injection defense, guardrails, rate limiting, logging, and monitoring generation quality.

Deployment: Containerization, multi-container setup with docker-compose, deployment scripts, and production checklist.

audience

This course is designed for

Software Engineers wanting to understand RAG systems
DevOps Professionals deploying AI systems
AI/ML Practitioners building production applications
Anyone interested in AI development

prerequisites

To get most out of this course, you should have:

Basic Python knowledge (familiar with functions, classes, libraries)
Understanding of APIs (REST, HTTP basics)
Comfortable using AI tools (Copilot or similar)

style

Our trainers have years of experience and will deliver the right mix of:

Interactive lectures: Concepts explained with real examples, demonstrations, Q&A sessions
Hands-on labs: Progressive labs building toward the complete system with guided exercises

Technical requirements

We recommend the following equipment:

Stable internet connection
Access to AI tools and services (e.g., OpenRouter, Groq, OpenAI API) (Free accounts may need to be created)
Browser compatible with modern web applications (e.g., Chrome)
If you're eager to try out demonstrated tools on your own: Code editor (e.g., VS Code) with sufficient hardware (GPU) and a Python installation