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  • Prompt Engineering
  • Introduction
    • LLM Settings
    • Basics of Prompting
    • Prompt Elements
    • General Tips for Designing Prompts
    • Examples of Prompts
  • Techniques
    • Zero-shot Prompting
    • Few-shot Prompting
    • Chain-of-Thought Prompting
    • Self-Consistency
    • Generate Knowledge Prompting
    • Tree of Thoughts
    • Retrieval Augmented Generation
    • Automatic Reasoning and Tool-use
    • Automatic Prompt Engineer
    • Active-Prompt
    • Directional Stimulus Prompting
    • ReAct
    • Multimodal CoT
    • Graph Prompting
  • Applications
    • Program-Aided Language Models
    • Generating Data
    • Generating Synthetic Dataset for RAG
    • Tackling Generated Datasets Diversity
    • Generating Code
    • Graduate Job Classification Case Study
    • Prompt Function
  • Models
    • Flan
    • ChatGPT
    • LLaMA
    • GPT-4
    • LLM Collection
  • Risks & Misuses
    • Adversarial Prompting
    • Factuality
    • Biases
  • Papers
  • Tools
  • Notebooks
  • Datasets
  • Additional Readings
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Models

Models

In this section, we will cover some of the recent language models and how they successfully apply the latest and most advanced prompting engineering techniques. In addition, we cover capabilities of these models on a range of tasks and prompting setups like few-shot prompting, zero-shot prompting, and chain-of-thought prompting. Understanding these capabilities are important to understand the limitations of these models and how to use them effectively.

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This section is under heavy development.

Prompt FunctionFlan

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