LLM Application Development with LangChain & LlamaIndex

Course Structure 

• Duration: 5 days Executive Bootcamp (Including full constultative Sessions with AI Experts)

• Onsite: At VIP Location

• Included: Buffet breakfast and Lunch included

• Access to the Catalyst AI Hub Africa Alumni Network

• 30 days of post-programme support

• 12 Hands-On Developer Labs

• 3 portfolio-ready AI applications — Document Q&A App, Autonomous Research Agent, and Deployed AI API

• Theory vs Practical: 20% / 80%

Ideal For

• Software developers and backend engineers ready to add AI to their skill set

• Full-stack developers who want to build and ship AI-powered products

• Technical founders building AI-native startups and SaaS applications

• Freelance developers looking to offer AI application development services

• Engineering students and bootcamp graduates entering the AI job market

• Any developer who has experimented with AI APIs and is ready to build something real

Description

This is the course for developers who want to go beyond using AI tools and start engineering AI-powered applications from scratch. Every session is code-first, hands-on, and focused on one outcome — shipping working AI applications. Participants learn the most in-demand developer frameworks in the industry today, building real applications that connect large language models to data, APIs, and user interfaces. No enterprise strategy, no business frameworks — pure engineering from start to finish.

What Participants Will Learn

By the end of this course, participants will be able to:

• Build and deploy production-ready AI applications using LangChain and LlamaIndex

• Design and implement Retrieval-Augmented Generation (RAG) systems that connect AI to any document or data source

• Create autonomous AI agents that reason, plan, and execute multi-step tasks using real-world tools and APIs

• Expose AI logic as a scalable REST API using FastAPI and deploy it to a live cloud environment

• Implement conversation memory, streaming responses, and error handling for reliable AI applications

• Connect large language models to vector databases for fast, accurate semantic search and retrieval

• Build three portfolio-ready projects — a Document Q&A App, an Autonomous Research Agent, and a deployed AI API