LlamaIndex Training Online

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:

• Basics of embeddings & vector databases
• Intro-level ML/NLP concepts

What You Will Learn

• 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