✦Sparkl
innovator · talks

LLMs In Depth

Explore how large language models process text, generate responses, and shape modern AI applications. Build technical fluency in tokenization, parameters, prompt engineering, and model behavior analysis.

20 modules·Difficulty: ★★★☆☆· 5 Free
Start module 1

Modules

  • 1
    Free8 min
    What Are Tokens?
    Discover how LLMs split text into tiny pieces called tokens to understand language 🔤
  • 2
    Free10 min
    Context Windows Explained
    Learn how much text an LLM can remember at once and why longer windows matter
  • 3
    Free9 min
    Temperature and Randomness
    Explore how temperature settings make AI responses more creative or more predictable
  • 4
    Free11 min
    Top-p Sampling Mechanics
    See how top-p controls word choice by selecting from the most likely options
  • 5
    Free10 min
    Embeddings and Vector Space
    Understand how words become numbers in multi-dimensional space so LLMs can compare meaning
  • 6
    Paid12 min
    Building Your First Prompt
    Write clear instructions that guide an LLM to produce exactly what you need
  • 7
    Paid11 min
    Few-Shot Learning Patterns
    Teach the model new tasks by showing a handful of example input-output pairs
  • 8
    Paid10 min
    Chain-of-Thought Reasoning
    Guide LLMs to think step-by-step through complex problems before answering
  • 9
    Paid9 min
    Role and Persona Assignment
    Assign the LLM a specific role to shape tone, expertise, and response style
  • 10
    Paid11 min
    System vs User Messages
    Learn the difference between high-level system instructions and turn-by-turn user input
  • 11
    Paid10 min
    Detecting Hallucinations
    Identify when an LLM invents facts and learn strategies to minimize false information
  • 12
    Paid12 min
    Bias in Language Models
    Examine how training data can introduce stereotypes and unfair patterns into outputs
  • 13
    Paid11 min
    Fine-Tuning Fundamentals
    Discover how additional training on specialized datasets customizes model behavior
  • 14
    Paid10 min
    Retrieval-Augmented Generation
    Combine LLMs with external knowledge bases to provide accurate, up-to-date answers
  • 15
    Paid12 min
    Transformer Architecture Overview
    Peek inside the self-attention mechanism that powers modern LLMs
  • 16
    Paid11 min
    Evaluating Output Quality
    Apply metrics like coherence, relevance, and factuality to judge LLM responses
  • 17
    Paid10 min
    Comparing Model Families
    Contrast different LLM architectures and understand their strengths for various tasks
  • 18
    Paid12 min
    Ethics and Responsible Use
    Explore principles for deploying LLMs safely, fairly, and transparently in real applications
  • 19
    Paid11 min
    Capstone: LLM Behavior Report
    Synthesize everything you learned to document how an LLM handles diverse prompts and tasks
  • 20
    Final exam25 min
    Final Assessment
    Demonstrate your mastery of tokenization, parameters, prompting, and responsible AI practices