Prerequisites
- Basic to intermediate Python
- Understanding of APIs & API keys
- Basic knowledge of LLMs
- Familiarity with RAG (Retrieval-Augmented Generation)
- Ability to set up a Python environment (pip, venv, IDE)
Helpful but not mandatory:
What You Will Learn
In this course, you will learn the below topics.
- How LlamaIndex works with Large Language Models (LLMs)
- Building Retrieval-Augmented Generation (RAG) applications
- Loading and indexing data from multiple sources (PDFs, docs, databases)
- Creating and using embeddings and vector indexes
- Connecting LlamaIndex with LLM APIs (OpenAI, etc.)
- Querying and retrieving relevant context efficiently
- Building chatbots and Q&A systems on custom data
- Evaluating and optimizing RAG performance
- Basic deployment and integration into applications
Tools and Technologies Covered
- LlamaIndex library (core Python package for indexing & retrieval)
- Python (programming language)
- LLM APIs (e.g., OpenAI, Anthropic, etc.)
- Embeddings models (to convert text into vectors)
- Vector Databases (e.g., Chroma, Pinecone, Milvus – depending on the course)
- Data sources: PDFs, text files, CSV/SQL databases
- Development tools: pip, virtual environments, IDEs (VS Code/PyCharm)
- Optional deployment/infra tools: Docker, cloud services (if included)
Career Opportunities After Completing LlamaIndex Training
- Generative AI Engineer
- AI / ML Engineer
- LLM Application Developer
- RAG Application Developer
- Data Engineer (AI-focused)
- AI Solutions Architect
- Chatbot / Conversational AI Developer
