Welcome to Module 6 — Large Language Models (LLMs).

In this lesson, you will understand how modern Large Language Models work and why they have become the foundation of many AI applications. You will explore the architecture behind LLMs, how models process language, how they are trained and adapted, and how developers use them through APIs and AI platforms.

Topics Covered:

1. What are Large Language Models?
   Understand what LLMs are, how they process language, and what makes them different from traditional AI and Machine Learning models.
2. Foundation Models
   Learn the concept of foundation models and how a single pretrained model can support multiple AI tasks and applications.
3. LLM Architecture
   Understand the high-level architecture of modern language models and how different components work together.
4. Transformers
   Learn the Transformer architecture and why it became a major foundation for modern language models.
5. Tokens and Tokenization
   Understand how text is converted into tokens and how tokenization affects the way an LLM processes information.
6. Embeddings
   Learn how text and other information can be represented as numerical vectors that capture semantic relationships.
7. Attention and Self-Attention
   Understand how attention mechanisms allow models to identify relationships between different parts of an input.
8. Context Windows
   Learn what a context window is and how the amount of available context affects an LLM's ability to process information.
9. Parameters
   Understand what model parameters represent and why parameter size is one of several factors that influence model capabilities.
10. Pretraining
    Learn how LLMs are trained on large datasets to learn language patterns, structures, relationships, and general knowledge.
11. Instruction Tuning
    Understand how pretrained models can be further trained to follow instructions and perform tasks more effectively.
12. Fine-Tuning
    Learn how models can be adapted for specific domains, tasks, or use cases using additional training data.
13. Inference
    Understand what happens when an LLM receives an input and generates a response during inference.
14. LLM Generation Controls
    Explore important generation settings such as:
    • Temperature
    • Top-K
    • Top-P
    • Maximum Output Tokens
15. LLM APIs
    Learn how developers interact with language models through APIs and integrate LLM capabilities into applications.
16. Open-Source and Open-Weight LLMs
    Understand the concept of openly available language models and how they differ from hosted proprietary models.
17. Hugging Face and Model Ecosystem
    Explore the role of platforms such as Hugging Face in discovering, using, sharing, and deploying AI models.
18. Running LLMs Locally
    Understand the basic concept of running language models on local machines and the factors that influence hardware requirements.
19. LLM Applications
    Explore real-world applications including:
    • AI Chatbots
    • Coding Assistants
    • Content Generation
    • Summarization
    • Translation
    • Research Assistants
    • Customer Support
    • Business Automation
20. LLM Limitations and Evaluation
    Understand hallucinations, outdated knowledge, bias, context limitations, computational requirements, and the importance of evaluating model outputs.

Mini Project:
Build an LLM-powered chatbot that accepts user questions, generates contextual responses, and provides a practical conversational AI experience using an LLM API or locally available model.

Learning Outcome:
By the end of this lesson, you will understand the fundamentals of LLMs, Transformers, tokens, embeddings, attention, context windows, training, fine-tuning, inference, and generation controls. You will also understand how LLMs are accessed through APIs and used to build real-world AI applications.

Lesson Resources:
Video Lesson + Presentation PDF + Quize