Large Language Models

LLM and Prompt Engineering

AI, ML
Natural Language Processing, NLP, Large Language Models, LLM, Prompt Engineering, Transfer Learning

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: