LLM and Prompt Engineering

Mon, 01 Jan 0001 00:00:00 +0000

LLMs and Prompt Engineering #

A Large Language Model extends neural language modelling through much larger datasets, many more parameters, broad pretraining and adaptation to many downstream tasks. Its central operation remains next-token prediction.

Learning Objectives #

Explain how neural language modelling develops into an LLM.
Describe the meaning of large, general-purpose and pretrained.
Explain how a prompt guides generation.
Distinguish zero-shot and few-shot prompting.
Compare prompting with model adaptation.

Big Picture #

flowchart TD
 A["Broad Text Data"] --> B["Large-scale Pretraining"]
 B --> C["General Language Model"]
 C --> D["Prompt or Adaptation"]
 D --> E["Task Output"]

 style A fill:#E1F5FE
 style B fill:#C8E6C9
 style C fill:#FFF9C4
 style D fill:#EDE7F6
 style E fill:#E1F5FE

1. From Neural Language Models to LLMs ☆ #

A neural language model learns a conditional probability for the next token:

Large Language Models on Arshad Siddiqui

LLM and Prompt Engineering

LLMs and Prompt Engineering #

Learning Objectives #

Big Picture #

1. From Neural Language Models to LLMs ☆ #