Introduction to Large Language Models
Chapter 1: Understanding Large Language Models
What is Artificial Intelligence? A Brief Overview
Introducing Natural Language Processing (NLP)
Defining Large Language Models (LLMs)
How LLMs Learn from Text Data
Examples of Tasks LLMs Can Perform
Common Misconceptions about LLMs
Quiz for Chapter 1
Chapter 2: The Mechanics of LLMs (Simplified)
Representing Words: Tokens and Embeddings
Predicting the Next Word: The Core Idea
The Role of Training Data Size
Understanding Model Parameters
Introduction to Transformer Architecture (High-Level)
How Context Influences Generation
Quiz for Chapter 2
Chapter 3: Communicating with LLMs: Prompts
What is a Prompt?
Basic Prompting Techniques
Providing Instructions Clearly
Giving Examples (Few-Shot Prompting)
Controlling Output Length and Format
Common Prompting Challenges
Practice: Crafting Your First Prompts
Quiz for Chapter 3
Chapter 4: A Look at Different LLMs
Overview of Foundational Models
General Purpose vs. Specialized Models
Open vs. Closed Models: What's the Difference?
Understanding Model Size and Capabilities
Accessing Models: APIs and Interfaces
Quiz for Chapter 4
Chapter 5: Using Pre-trained LLMs
What are Pre-trained Models?
Finding and Choosing an LLM Service
Interacting via Web Interfaces
Introduction to Using LLM APIs
Sending Your First API Request
Interpreting LLM Responses
Hands-on Practical: Simple Text Generation Task
Quiz for Chapter 5
Common Prompting Challenges
New · Open Source
Kerb - LLM Development Toolkit
Python toolkit for building production-ready LLM applications. Modular utilities for prompts, RAG, agents, structured outputs, and multi-provider support.
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- On the Dangers of Stochastic Parrots: Can Language Models Be Too Big?, Emily M. Bender, Timnit Gebru, Angelina McMillan-Major, Shmargaret Shmitchell, 2021 Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency (Association for Computing Machinery) DOI: 10.1145/3442188.3445922 - A foundational paper discussing the ethical implications and potential societal biases embedded within large language models due to their training data and design, directly relevant to the 'Bias in Responses' challenge.
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