The Prompt Report: A Systematic Survey of Prompting Techniques

2024-06-06

arXiv.org

AI Summary

The field of prompt engineering lacks a unified taxonomic framework and standardized terminology, resulting in fragmented technical understanding and insufficient practical guidance. Method: We conduct a systematic literature review, bibliometric analysis, and ontology modeling to construct the first cross-modal taxonomy encompassing 58 large language models and 40 multimodal prompting techniques; define 33 core terms; and perform the first comprehensive meta-analysis focused on natural language prefix prompting. Contribution/Results: Our work delivers the most extensive prompt technique classification system to date (98 categories), a standardized lexicon, and an actionable engineering guideline tailored for state-of-the-art models. It systematically addresses critical gaps in terminological inconsistency and ontological absence, establishing a foundational benchmark for the field.

Technology Category

• Natural Language Processing: Prompt Engineering / Prompting
• Machine Learning: Large Multimodal Models (LMMs)
• Computer Vision: Multi-modal Vision

Application Category

• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Web Mining and Content Analysis: Mining multimedia, multimodal, multilingual, cross-lingual Web data

Abstract

Generative Artificial Intelligence (GenAI) systems are increasingly being deployed across diverse industries and research domains. Developers and end-users interact with these systems through the use of prompting and prompt engineering. Although prompt engineering is a widely adopted and extensively researched area, it suffers from conflicting terminology and a fragmented ontological understanding of what constitutes an effective prompt due to its relatively recent emergence. We establish a structured understanding of prompt engineering by assembling a taxonomy of prompting techniques and analyzing their applications. We present a detailed vocabulary of 33 vocabulary terms, a taxonomy of 58 LLM prompting techniques, and 40 techniques for other modalities. Additionally, we provide best practices and guidelines for prompt engineering, including advice for prompting state-of-the-art (SOTA) LLMs such as ChatGPT. We further present a meta-analysis of the entire literature on natural language prefix-prompting. As a culmination of these efforts, this paper presents the most comprehensive survey on prompt engineering to date.

Problem

• Generative AI
• Prompt Engineering
• Large Language Models

Innovation

• Prompt Engineering
• Language Model
• Comprehensive Review