Title: How Llms Work
Course: LLM_Prompt_Engineering_For_Developers > Section: Module 85: How Llms Work

How LLMs Work

LLMs are typically built using architectures such as the Transformer, which relies on mechanisms called attention. Here’s a simplified view of how they generate responses:

1. Tokenization: The input text is broken down into smaller chunks called tokens (words or subwords). For example, the phrase “Hello, world!” might be tokenized into ["Hello”, “,”, “world”, “!"].
2. Embedding: Each token is converted into a numerical format (embedding), capturing semantic meanings based on their context.
3. Attention Mechanism: The model then uses attention mechanisms to weigh the importance of different words in relation to one another, which helps in understanding context and significance.
4. Generating Output: Finally, the model generates responses by predicting the next token iteratively until it completes a coherent sentence or paragraph.

Example of LLM in Action:

Consider a simple prompt:
Prompt: “What are the benefits of studying programming?”

LLM Response:
“Studying programming offers numerous benefits, including problem-solving skills, career opportunities in a tech-driven world, and the ability to create applications that can improve lives. It enhances logical thinking and can be applied across various disciplines.”

In this instance, the LLM has understood the prompt and generated a detailed response that demonstrates its capabilities.

Applications of LLMs