Prompt Engineering, Part 1 - Introduction

jashburn8020 · Tue, 11 Jun 2024

ai

A brief introduction to prompt engineering and a taxonomy of techniques.

This series of articles briefly describes the various techniques used in prompt engineering.

Note: The info on this page is mainly summarised from the references listed below, except where indicated otherwise.

Introduction

  • Technique for enhancing the capabilities of pre-trained large language models (LLMs) and vision-language models (VLMs).
  • A mechanism to fine-tune model outputs through carefully crafted instructions, enables these models to excel across diverse tasks and domains.
  • Involves strategically designing task-specific instructions (prompts), to guide model output without altering parameters.
    • Prompt: Natural language text describing the task that an AI should perform.
  • Different from traditional paradigms, where model retraining or extensive fine-tuning is often required for task-specific performance.
  • A prompt for a text-to-text language model can be:
    • a query, e.g., “what is Fermat’s little theorem?”,
    • a command, e.g., “write a poem about leaves falling”, including assigning a role such as “act as a native French speaker”, or
    • a longer statement including context, instructions, and conversation history.
  • A prompt may include a few examples for which a model to learn.
  • With a text-to-image or a text-to-audio model, a typical prompt is a description of a desired output, e.g.,
    • “a high-quality photo of an astronaut riding a horse”
    • “lo-fi slow BPM electro chill with organic samples”.
  • Prompting a text-to-image model
    • may involve adding, removing, emphasizing and re-ordering words
    • to achieve a desired subject, style, layout, lighting, and aesthetic.

Taxonomy of Techniques

@startmindmap * Prompt Engineering * New Tasks Without Extensive Training * Zero-Shot Prompting * Few-Shot Prompting * Reasoning and Logic * Chain-of-Thought (CoT) Prompting * Automatic Chain-of-Thought (Auto-CoT) * Self-Consistency * Logical CoT (LogiCoT) Prompting * Chain-of-Symbol (CoS) Prompting * Tree-of-Thoughts (ToT) Prompting * Graph-of-Thought (GoT) Prompting * System 2 Attention Prompting * Thread of Thought (ThoT) Prompting * Chain of Table Prompting * Reduce Hallucination * Retrieval Augmented Generation (RAG) * ReAct Prompting * Chain-of-Verification (CoVe) * Chain-of-Note (CoN) Prompting * Chain-of-Knowledge (CoK) Prompting * User Interaction * Active-Prompt * Fine-Tuning and Optimization * Automatic Prompt Engineer (APE) * Knowledge-Based Reasoning and Generation * Automatic Reasoning and Tool-use (ART) * Improving Consistency and Coherence * Contrastive Chain-of-Thought Prompting (CCoT) * Managing Emotions and Tone * Emotion Prompting * Code Generation and Execution * Scratchpad Prompting * Program of Thoughts (PoT) Prompting * Structured Chain-of-Thought (SCoT) Prompting * Chain of Code (CoC) Prompting * Optimization and Efficiency * Optimization by Prompting * Understanding User Intent * Rephrase and Respond (RaR) Prompting * Metacognition and Self-Reflection * Take a Step Back Prompting @endmindmap

References

  • Sahoo, P., Singh, A. K., Saha, S., Jain, V., Mondal, S., & Chadha, A. (2024). A Systematic Survey of Prompt Engineering in Large Language Models: Techniques and Applications. arXiv preprint arXiv:2402.07927.
  • Wikipedia Contributors. (2024, June 5). Prompt engineering. Wikipedia; Wikimedia Foundation. https://en.wikipedia.org/wiki/Prompt_engineering

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