Section 1
LLM Fundamentals
Preview
Preview: LLM Fundamentals
- 4 of 29 lesson topics
- 1 overview segment
- 3 core concepts
- 2 exam tips
Lesson Topics
- LLM Text Generation From Context
- Context Window Capacity
- When an LLM Is Appropriate
- Enterprise Knowledge Grounding
Overview
This section establishes the foundational knowledge required to understand how large language models (LLMs) operate and when they are appropriate for various tasks. It covers core concepts like tokenization, attention mechanisms, and the limitations of LLMs, emphasizing the crucial distinction between context and training data.
Core Concepts
- **Autoregressive Next-Token Prediction:** LLMs generate text sequentially, predicting the next token based on the prompt and previously generated tokens. This iterative process is fundamental to their operation.
- **Context Window Capacity:** LLMs have a limited context window, which defines the maximum number of tokens they can process in a single request. Exceeding this limit leads to truncation or degraded performance.
- **Token Embeddings:** Tokens (words or subwords) are represented as numerical vectors (embeddings) that capture semantic meaning, enabling the model to process them mathematically.
Exam Tips
- Prioritize understanding the limitations of LLMs, especially regarding context window capacity and the potential for hallucinations.
- Pay close attention to questions involving prompt engineering and how to optimize prompts for specific tasks.