Catalyze AI: From Python to GenAI & Agentic AI
Catalyze AI is an end-to-end program that takes you from Python fundamentals to building production-ready Generative AI and multi-agent systems. You’ll learn how modern AI applications are designed, built — not just how to use them.
What You’ll Learn:
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Write clean, backend-ready Python for AI workflows
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Understand how LLMs like GPT, Claude, and Gemini actually work
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Master prompt engineering, embeddings, and RAG pipelines
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Build a Capstone Multi Agent GenAI system powered by vector databases
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Design and orchestrate multi-agent AI systems using LangChain and LangGraph
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Create real GenAI applications with Streamlit
Career Outcome:
AI ENGINEER
Build and deploy AI models into production applications.
AI RESEARCH ENGINEER
They are responsible for advancing state-of-the-art AI systems, conducting experiments, and transforming research findings into scalable, production-ready applications.
GENAI ENGINEER
Design, build, and deploy advanced AI models—primarily Large Language Models (LLMs) and Generative Adversarial Networks (GANs) transforming research findings into scalable, production-ready applications.
AI BACKEND DEVELOPER
Designing API backends, preprocessing data for AI models, building server-side logic, and integrating AI models into applications
PYTHON DEVELOPER
Creating server-side logic, developing REST APIs, automating tasks, and implementing data-driven solutions using frameworks like Django, Flask, or FastAPI.
Program Outline:
Curriculam Road map
Chapter 1 – Python Basics
Chapter 2 – Control Flow
Chapter 3- Functions & Modular Code
Chapter 4 - Python Collections & Comprehensions
Chapter 5 - Advanced Python & Database Concepts
Chapter 1 - Introduction to Generative AI
Chapter 2 - Large Language Models Landscape
Chapter 3 - Prompt Engineering
Chapter 1: Foundations of Multi-Agent AI Systems
Chapter 2 - Embeddings & Vector Databases
Chapter 3 - Retrieval Augmented Generation (RAG)
Chapter 4: LangChain – Building Tool - Using & Agentic AI Systems
Chapter 5: LangGraph – Designing Stateful Multi-Agent Workflows
Chapter 6: Streamlit for Multi-Agent GenAI Applications
Tools Covered:
TECH STACK YOU’LL MASTER
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PROGRAMMING LANGUAGE:
Python
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AI FRAMEWORKS & CONCEPTS
LANGCHAIN
LANGGRAPH
RAG (RETRIEVAL-AUGMENTED GENERATION) -
DATABASES
POSTGRESQL
VECTOR DBS (CHROMADB, QDRANT) -
TOOLS & PROJECT MANAGEMENT
GIT
TRELLO
SCRUM (AGILE METHODOLOGY – SPRINTS, STAND-UPS, SPRINT PLANNING, RETROSPECTIVES)
Ready to Accelerate Your Career
Join the next cohort of Catalyze AI and transform from a coder to an AI Architect.