A Systematic Survey of Prompt Engineering in Large Language Models: Techniques and Applications
By Pranab Sahoo, Ayush Kumar Singh, Sriparna Saha, Vinija Jain, Samrat Mondal, Aman Chadha. arXiv.org, 2024
948 citations recorded by the source corpus.
Prompt engineering has emerged as an indispensable technique for extending the capabilities of large language models (LLMs) and vision-language models (VLMs). This approach leverages task-specific instructions, known as prompts, to enhance model efficacy without modifying the core model parameters. Rather than updating the model parameters, prompts allow seamless integration of pre-trained models into downstream tasks by eliciting desired model behaviors solely based on the given prompt. Prompts can be natural language instructions that provide context to guide the model or learned vector representations that activate relevant knowledge. This burgeoning field has enabled success across various applications, from question-answering to commonsense reasoning. However, there remains a lack of systematic organization and understanding of the diverse prompt engineering methods and techniques. This survey paper addresses the gap by providing a structured overview of recent advancements in prompt engineering, categorized by application area. For each prompting approach, we provide a summary detailing the prompting methodology, its applications, the models involved, and the datasets utilized. We also delve into the strengths and limitations of each approach and include a taxonomy diagram and table summarizing datasets, models, and critical points of each prompting technique. This systematic analysis enables a better understanding of this rapidly developing field and facilitates future research by illuminating open challenges and opportunities for prompt engineering.
Subfields: Artificial Intelligence
View Paper: https://www.semanticscholar.org/paper/31d2ccff82e313eb5c1620c44bb8322da4a38513
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In Artificial Intelligence
Citations in the corpus
- Large language models for generative information extraction: a survey (2023, neutral): “These challenges include the misalignment between natural language output and structured form [6], hallucination problem in LLMs [48], contextual dependence, high computational resource requirements [49], di ffi culties in updating internal knowledge [50], etc.”
- Black-box model classification under the discriminative factorization (2026, unknown): “Each covers one of the three most popular way to change the behavior of a system (parameter-efficient fine-tuning [14], system prompts [32], and retrieved context [12]).”
- Traj-CoA: Patient Trajectory Modeling via Chain-of-Agents for Lung Cancer Risk Prediction (2025, unknown): “[ 18, 45] In stage one, a series of worker agents W i sequentially process each chunk c i .”
- Understanding Generative AI Content with Embedding Models (2024, background): “We remark that while it is common knowledge that prompting and fine-tuning techniques can drastically change the qualities of generated output [48, 49], these numerical embeddings can be used to develop further prompting analytics for other applications.”
- Mapping Scientific Knowledge in Systematic Literature Reviews Through Embeddings and Bibliometric Techniques (2026, unknown): “Techniques such as zero-shot, few-shot, and chain-of-thought prompting, which improve label quality, are evolving [48], [49].”
- Query-efficient model evaluation using cached responses (2026, unknown): “…to produce new model behavior – via parameter efficient fine-tuning (Han et al., 2024), instruction tuning (Zhang et al., 2025), model merging (Matena & Raffel, 2022), distillation (Gou et al., 2021), prompt engineering (Sahoo et al., 2025), etc. – making it infeasible to evaluate each new variant.”