AI Builder Foundations: From Gen AI User to Confident AI Builder

About Course

AI tools like ChatGPT, Gemini, and Claude are changing how professionals work — but most people are only scratching the surface of what’s possible. This course helps you go deeper, without needing a technical background to do it.

Building an AI Mindset is a practical, beginner-friendly course that gives working professionals a genuine understanding of how modern AI works — and how to make it work for you. You’ll explore the foundations of Large Language Models (LLMs), understand how Retrieval-Augmented Generation (RAG) makes AI smarter and more reliable, and learn how to use today’s most powerful AI tools far beyond basic chat.

You’ll also get a gentle introduction to Python — just enough to customise AI tools, automate repetitive tasks, and unlock capabilities that most professionals don’t even know exist. No prior coding experience needed. Every concept is explained in plain language, and every lesson connects directly to real workplace situations.

By the end of this course you won’t just be an AI user — you’ll be an AI-informed professional who thinks differently about how work gets done, makes smarter tool choices and brings genuine AI confidence.

Understand how Large Language Models (LLMs) actually work — beyond the buzzwords
Grasp the concept of Retrieval-Augmented Generation (RAG) and why it makes AI responses more reliable and context-aware
Use AI tools like ChatGPT, Gemini, and Claude effectively — going far beyond basic chat to unlock their real professional potential
Apply beginner-friendly Python coding to customise and extend GPT capabilities for your specific needs
Identify the right AI tools for specific professional use cases and evaluate them critically
Develop an AI mindset — a structured way of thinking about where AI adds value and where it doesn't
Avoid common mistakes professionals make when adopting AI tools at work
Apply AI concepts to real workplace scenarios through hands-on assignments
Communicate AI concepts clearly to teammates, managers, and stakeholders who are new to the topic

Course Content

Module 0 — Your AI Builder Mindset
Get oriented, set up your environment, and understand how AI systems actually work. This module is light, motivating, and gets you making your first API call fast.

Why its important to get the AI mindset ?

01:04
Mapping Your Work to AI Opportunities
Setting Up Your Dev Environment
How AI Systems Actually Work (Big Picture)

Module 1 — LLM Fundamentals (Only What Builders Need)
Learn the core concepts behind LLMs that every builder needs — tokens, context windows, temperature, prompts, and costs. Practical intuition, not research depth.

What is an LLM? (Builder’s Mental Model)

02:24
What is an LLM? Quiz
Lab Exercise 1
Tokens, Context Windows & Costs

03:33
Tokens, Context Windows & Cost Awareness Quiz
Lab Exercise 2
Temperature & Model Parameters

00:00
Temperature & Model Parameters Quiz
Lab Exercise 3
System vs User Prompts & Hallucinations

00:00
System Prompts, User Prompts & Hallucination Quiz
Lab Exercise 4

Module 2 — Prompt Engineering for Builders
Master practical prompt engineering techniques that directly improve your AI apps. Every technique is applied to a real-world use case, not abstract exercises.

Prompt Structure Framework

00:00
Prompt Structure Framework Quiz
Lab Exercise 1
Few-Shot Examples & Structured JSON Output

00:00
Few-Shot Prompting Quiz
Lab Exercise 2
Prompt Chaining & Prompt Templates

00:00
Prompt Chaining & Templates Quiz
Lab Exercise 3
Prompt Debugging Patterns — Mini Lab

00:00
Prompt Debugging & Optimization Quiz
Lab Exercise 4

Module 3 — Working with LLM APIs
Graduate from notebooks to real Python scripts. Learn to make API calls, handle errors, manage rate limits, and stream responses. Build your first CLI AI tool.

What is an API? (10-Minute Explanation)

00:00
What is an API Quiz
Lab Exercise 1
Making Your First Real API Call in VS Code

00:00
First Real API Call Quiz
Lab Exercise 2
Handling Errors, Retries & Rate Limits

00:00
API Errors, Rate Limits & Streaming Quiz
Lab Exercise 3
Streaming Responses — Mini Project: Build a Simple AI Tool

00:00
Build Your First CLI AI Tool Quiz
Lab Exercise 4

Module 4 — Build Your First Real AI App
Build a multi-turn chat app with memory using Streamlit. Your first visual, interactive AI application — running in your browser, built entirely in Python.

Introduction to Streamlit

00:00
Introduction to Streamlit Quiz
Lab Exercise 1
Conversation State & Context Window Memory

00:00
Conversation State & Context Memory Quiz
Lab Exercise 2
Build: Full Chat App with Memory + Logging

00:00
Multi-Turn AI Chat Application – Implementation Test
Lab Exercise 3

Module 5 — RAG for Beginners
Understand why RAG exists, how it works, and build a complete document Q&A system from scratch using your own documents. One vector database, one clear pipeline.

Why RAG Exists & How It Works

00:00
Why RAG Exists & How It Works Quiz
Lab Exercise 1
Embeddings & Chunking Basics

00:00
Embeddings & Chunking Basics Quiz
Lab Exercise 2
Vector Databases — Introduction to ChromaDB

00:00
Vector Database Fundamentals Quiz
Lab Exercise 3
Build: Document Q&A System (End-to-End)

00:00
End-to-End Document Q&A System Quiz
Lab Exercise 4

Module 6 — Tool Use & Function Calling
Give your AI app the ability to take actions in the real world. Build two tools, connect them to an LLM, and watch the model decide which tool to call automatically.

What is Tool Calling? (Concept + How it Works)

00:00
What is Tool Calling? Quiz
Lab Exercise 1
Build Tool 1: Web Search

00:00
Build Web Search Tool Quiz
Lab Exercise 2
Build Tool 2: Calculator / Database Lookup — Mini Project

00:00
Build Tool 2 (Calculator & Database Lookup) Quiz
Lab Exercise 3

Module 7 — Introduction to AI Agents
Understand what an agent actually is, build one using the ReAct pattern, and experience the difference between a chatbot that responds and an agent that acts.

Module 8 — Deploy Your First AI System
Take your local Streamlit app live on the internet. Free deployment, no server knowledge required, shareable link in under 10 minutes.

Part 1 Capstone — Build & Deploy Your Own AI App
Apply everything from Part 1 to build and deploy one complete AI application connected to your real work. This becomes your first portfolio project.

Add this certificate to your resume to demonstrate your skills & increase your chances of getting noticed.

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About Course

What Will You Learn?

Course Content

Module 0 — Your AI Builder Mindset Get oriented, set up your environment, and understand how AI systems actually work. This module is light, motivating, and gets you making your first API call fast.

Why its important to get the AI mindset ?

Mapping Your Work to AI Opportunities

Setting Up Your Dev Environment

How AI Systems Actually Work (Big Picture)

Module 1 — LLM Fundamentals (Only What Builders Need) Learn the core concepts behind LLMs that every builder needs — tokens, context windows, temperature, prompts, and costs. Practical intuition, not research depth.

What is an LLM? (Builder’s Mental Model)

What is an LLM? Quiz

Lab Exercise 1

Tokens, Context Windows & Costs

Tokens, Context Windows & Cost Awareness Quiz

Lab Exercise 2

Temperature & Model Parameters

Temperature & Model Parameters Quiz

Lab Exercise 3

System vs User Prompts & Hallucinations

System Prompts, User Prompts & Hallucination Quiz

Lab Exercise 4

Module 2 — Prompt Engineering for Builders Master practical prompt engineering techniques that directly improve your AI apps. Every technique is applied to a real-world use case, not abstract exercises.

Prompt Structure Framework

Prompt Structure Framework Quiz

Lab Exercise 1

Few-Shot Examples & Structured JSON Output

Few-Shot Prompting Quiz

Lab Exercise 2

Prompt Chaining & Prompt Templates

Prompt Chaining & Templates Quiz

Lab Exercise 3

Prompt Debugging Patterns — Mini Lab

Prompt Debugging & Optimization Quiz

Lab Exercise 4

Module 3 — Working with LLM APIs Graduate from notebooks to real Python scripts. Learn to make API calls, handle errors, manage rate limits, and stream responses. Build your first CLI AI tool.

What is an API? (10-Minute Explanation)

What is an API Quiz

Lab Exercise 1

Making Your First Real API Call in VS Code

First Real API Call Quiz

Lab Exercise 2

Handling Errors, Retries & Rate Limits

API Errors, Rate Limits & Streaming Quiz

Lab Exercise 3

Streaming Responses — Mini Project: Build a Simple AI Tool

Build Your First CLI AI Tool Quiz

Lab Exercise 4

Module 4 — Build Your First Real AI App Build a multi-turn chat app with memory using Streamlit. Your first visual, interactive AI application — running in your browser, built entirely in Python.

Introduction to Streamlit

Introduction to Streamlit Quiz

Lab Exercise 1

Conversation State & Context Window Memory

Conversation State & Context Memory Quiz

Lab Exercise 2

Build: Full Chat App with Memory + Logging

Multi-Turn AI Chat Application – Implementation Test

Lab Exercise 3

Module 5 — RAG for Beginners Understand why RAG exists, how it works, and build a complete document Q&A system from scratch using your own documents. One vector database, one clear pipeline.

Why RAG Exists & How It Works

Why RAG Exists & How It Works Quiz

Lab Exercise 1

Embeddings & Chunking Basics

Embeddings & Chunking Basics Quiz

Lab Exercise 2

Vector Databases — Introduction to ChromaDB

Vector Database Fundamentals Quiz

Lab Exercise 3

Build: Document Q&A System (End-to-End)

End-to-End Document Q&A System Quiz

Lab Exercise 4

Module 6 — Tool Use & Function Calling Give your AI app the ability to take actions in the real world. Build two tools, connect them to an LLM, and watch the model decide which tool to call automatically.

What is Tool Calling? (Concept + How it Works)

What is Tool Calling? Quiz

Lab Exercise 1

Build Tool 1: Web Search

Build Web Search Tool Quiz

Lab Exercise 2

Build Tool 2: Calculator / Database Lookup — Mini Project

Build Tool 2 (Calculator & Database Lookup) Quiz

Lab Exercise 3

Module 0 — Your AI Builder Mindset
Get oriented, set up your environment, and understand how AI systems actually work. This module is light, motivating, and gets you making your first API call fast.

Module 1 — LLM Fundamentals (Only What Builders Need)
Learn the core concepts behind LLMs that every builder needs — tokens, context windows, temperature, prompts, and costs. Practical intuition, not research depth.

Module 2 — Prompt Engineering for Builders
Master practical prompt engineering techniques that directly improve your AI apps. Every technique is applied to a real-world use case, not abstract exercises.

Module 3 — Working with LLM APIs
Graduate from notebooks to real Python scripts. Learn to make API calls, handle errors, manage rate limits, and stream responses. Build your first CLI AI tool.

Module 4 — Build Your First Real AI App
Build a multi-turn chat app with memory using Streamlit. Your first visual, interactive AI application — running in your browser, built entirely in Python.

Module 5 — RAG for Beginners
Understand why RAG exists, how it works, and build a complete document Q&A system from scratch using your own documents. One vector database, one clear pipeline.

Module 6 — Tool Use & Function Calling
Give your AI app the ability to take actions in the real world. Build two tools, connect them to an LLM, and watch the model decide which tool to call automatically.

Module 7 — Introduction to AI Agents
Understand what an agent actually is, build one using the ReAct pattern, and experience the difference between a chatbot that responds and an agent that acts.

Module 8 — Deploy Your First AI System
Take your local Streamlit app live on the internet. Free deployment, no server knowledge required, shareable link in under 10 minutes.

Part 1 Capstone — Build & Deploy Your Own AI App
Apply everything from Part 1 to build and deploy one complete AI application connected to your real work. This becomes your first portfolio project.