GenAI Fundamentals

Course Overview

This foundational course demystifies the core technologies behind modern AI. You'll understand not just how to use these systems, but how they work internally. We'll explore transformer architectures, attention mechanisms, and the training processes that create models like GPT-4 and Claude.

Neural Network Foundations

Deep learning basics, backpropagation, and the building blocks of modern AI architectures.

Perceptrons Activation Functions Loss Functions

Transformer Architecture

The revolutionary architecture that powers LLMs. Attention is all you need.

Self-Attention Multi-Head Attention Positional Encoding

Large Language Models

How GPT, Claude, and Llama work. Tokenization, embeddings, and generation.

Tokenization Embeddings Autoregressive Generation

Diffusion Models

The math behind Stable Diffusion, DALL-E, and Midjourney image generation.

Noise Scheduling U-Net CLIP

Prompt Engineering

The art and science of communicating effectively with AI models.

Chain of Thought Few-Shot Learning System Prompts

Model Evaluation

Benchmarks, metrics, and how to assess model quality and capabilities.

Perplexity BLEU/ROUGE Human Evaluation

Development Environment

Set up a professional AI development environment with VS Code Insiders and modern AI coding assistants.

Why VS Code Insiders?

VS Code Insiders provides early access to the latest features, including improved AI integrations, better extension APIs, and cutting-edge language support. For AI development, staying on the bleeding edge is crucial.

                    install-vscode-insiders.sh
                    bash
                

# Install VS Code Insiders on Ubuntu/Debian
wget -qO- https://packages.microsoft.com/keys/microsoft.asc | gpg --dearmor > packages.microsoft.gpg
sudo install -D -o root -g root -m 644 packages.microsoft.gpg /etc/apt/keyrings/packages.microsoft.gpg
sudo sh -c 'echo "deb [arch=amd64 signed-by=/etc/apt/keyrings/packages.microsoft.gpg] \
  https://packages.microsoft.com/repos/code stable main" > /etc/apt/sources.list.d/vscode.list'
sudo apt update && sudo apt install code-insiders

# Essential extensions for AI development
code-insiders --install-extension ms-python.python
code-insiders --install-extension ms-toolsai.jupyter
code-insiders --install-extension GitHub.copilot
code-insiders --install-extension saoudrizwan.claude-dev
                

AI Coding Assistants

Modern AI development leverages multiple assistants for different tasks:

Tool	Best For	Setup
Claude Code	Complex reasoning, architecture decisions, code review	`npm install -g @anthropic-ai/claude-code`
GitHub Copilot	Inline completions, boilerplate code, quick suggestions	VS Code Extension + GitHub subscription
Aider	Git-aware coding, multi-file changes, refactoring	`pip install aider-chat`
Gemini Code Assist	Google Cloud integrations, large context windows	VS Code Extension + Google Cloud

                    settings.json (VS Code Insiders)
                    json
                

{
  "python.defaultInterpreterPath": "~/.local/share/uv/python",
  "editor.inlineSuggest.enabled": true,
  "github.copilot.enable": {
    "*": true,
    "markdown": true,
    "plaintext": false
  },
  "editor.fontSize": 14,
  "editor.fontFamily": "'JetBrains Mono', 'Fira Code', monospace",
  "editor.fontLigatures": true,
  "workbench.colorTheme": "One Dark Pro"
}
                

Foundational Design Patterns

Before diving into AI, understand the software engineering patterns that underpin production systems. These creational patterns are essential for building maintainable AI applications.

Singleton

Single instance for model loaders, configuration managers, and database connections. Critical for expensive AI model initialization.

Factory Method

Create different AI backends (OpenAI, Anthropic, local models) through a unified interface without coupling to specific implementations.

Builder

Construct complex prompt templates, model configurations, and pipeline setups step by step with validation.

                    patterns/model_factory.py
                    python
                

from abc import ABC, abstractmethod
from functools import lru_cache

# Singleton pattern for expensive model loading
class ModelRegistry:
    _instance = None
    _models: dict = {}

    def __new__(cls):
        if cls._instance is None:
            cls._instance = super().__new__(cls)
        return cls._instance

    def get_model(self, name: str):
        if name not in self._models:
            self._models[name] = self._load_model(name)
        return self._models[name]

# Factory Method pattern for AI providers
class LLMProvider(ABC):
    @abstractmethod
    def generate(self, prompt: str) -> str:
        pass

class AnthropicProvider(LLMProvider):
    def generate(self, prompt: str) -> str:
        return self.client.messages.create(...)

class LLMFactory:
    @staticmethod
    def create(provider: str) -> LLMProvider:
        providers = {
            "anthropic": AnthropicProvider,
            "openai": OpenAIProvider,
            "ollama": OllamaProvider,
        }
        return providers[provider]()
                

Hands-On Exercises

Implement a minimal transformer attention mechanism from scratch in PyTorch
Build a BPE tokenizer and understand subword tokenization
Create a prompt template builder using the Builder pattern
Implement a model factory that switches between OpenAI, Anthropic, and Ollama
Set up VS Code Insiders with Claude Code and compare with Copilot suggestions
Fine-tune a small model (like DistilBERT) on a custom classification task
Evaluate model outputs using automated metrics and human feedback

Prerequisites

This course assumes familiarity with Python, basic linear algebra (matrices, vectors), and fundamental programming concepts. If you're new to Python, complete a Python fundamentals course first.

Resources

Required Reading

Attention Is All You Need (Vaswani et al.)
The Illustrated Transformer
DeepLearning.AI Courses

Tools & Libraries

PyTorch - Deep learning framework
HuggingFace Transformers
LangChain - LLM application framework

Ready to Master Generative AI?

This is just the beginning. Continue your journey with our advanced courses on AI-Assisted Development and Building AI Agents.

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Generative AI
Fundamentals

Course Overview

Neural Network Foundations

Transformer Architecture

Large Language Models

Diffusion Models

Prompt Engineering

Model Evaluation

Development Environment

Why VS Code Insiders?

AI Coding Assistants

Foundational Design Patterns

Singleton

Factory Method

Builder

Hands-On Exercises

Prerequisites

Resources

Required Reading

Tools & Libraries

Ready to Master Generative AI?

Ask iSeeCI

Generative AI Fundamentals

Course Overview

Neural Network Foundations

Transformer Architecture

Large Language Models

Diffusion Models

Prompt Engineering

Model Evaluation

Development Environment

Why VS Code Insiders?

AI Coding Assistants

Foundational Design Patterns

Singleton

Factory Method

Builder

Hands-On Exercises

Prerequisites

Resources

Required Reading

Tools & Libraries

Ready to Master Generative AI?

Ask iSeeCI

Generative AI
Fundamentals