Prompt Engineering Guide🎓 Prompt Engineering Course🎓 Prompt Engineering CourseServicesServicesAboutAbout
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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
    • Prompt Chaining
    • Tree of Thoughts
    • Retrieval Augmented Generation
    • Automatic Reasoning and Tool-use
    • Automatic Prompt Engineer
    • Active-Prompt
    • Directional Stimulus Prompting
    • Program-Aided Language Models
    • ReAct
    • Multimodal CoT
    • Graph Prompting
  • Applications
    • Function Calling
    • 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
    • Mistral 7B
    • Gemini
    • Phi-2
    • LLM Collection
  • Risks & Misuses
    • Adversarial Prompting
    • Factuality
    • Biases
  • LLM Research Findings
    • Trustworthiness in LLMs
  • Papers
  • Tools
  • Notebooks
  • Datasets
  • Additional Readings
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Applications

LLM Applications

In this section, we will cover advanced and interesting ways we can use prompt engineering to perform useful and more advanced tasks with large language models (LLMs).

Graph PromptingFunction Calling

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