Unleashing the potential of prompt engineering for large language models | Paper Citation Record

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A Systematic Survey of Prompt Engineering in Large Language Models: Techniques and Applications cites this paper.

A Systematic Survey of Prompt Engineering in Large Language Models: Techniques and Applications Unleashing the potential of prompt engineering for large language models

Reference 3

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Sora: A Review on Background, Technology, Limitations, and Opportunities of Large Vision Models cites this paper.

Sora: A Review on Background, Technology, Limitations, and Opportunities of Large Vision Models Unleashing the potential of prompt engineering for large language models

Reference 85

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Automated Design of Agentic Systems cites this paper.

Automated Design of Agentic Systems Unleashing the potential of prompt engineering for large language models

Reference 137

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ChatHTTPFuzz: Large Language Model-Assisted IoT HTTP Fuzzing cites this paper.

ChatHTTPFuzz: Large Language Model-Assisted IoT HTTP Fuzzing Unleashing the potential of prompt engineering for large language models

Reference 3

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Education in the Era of Neurosymbolic AI cites this paper.

Education in the Era of Neurosymbolic AI Unleashing the potential of prompt engineering for large language models

Reference 13

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CATP-LLM: Empowering Large Language Models for Cost-Aware Tool Planning cites this paper.

CATP-LLM: Empowering Large Language Models for Cost-Aware Tool Planning Unleashing the potential of prompt engineering for large language models

Reference 7

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Probing the limitations of multimodal language models for chemistry and materials research cites this paper.

Probing the limitations of multimodal language models for chemistry and materials research Unleashing the potential of prompt engineering for large language models

Reference 25

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Engineering AI Judge Systems cites this paper.

Engineering AI Judge Systems Unleashing the potential of prompt engineering for large language models

Reference 19

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OBI-Bench: Can LMMs Aid in Study of Ancient Script on Oracle Bones? cites this paper.

OBI-Bench: Can LMMs Aid in Study of Ancient Script on Oracle Bones? Unleashing the potential of prompt engineering for large language models

Reference 2022

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Observation 2c38c2fa-73ab-4a11-a1ea-766015b29073 · inbound

System Test Case Design from Requirements Specifications: Insights and Challenges of Using ChatGPT cites this paper.

System Test Case Design from Requirements Specifications: Insights and Challenges of Using ChatGPT Unleashing the potential of prompt engineering for large language models

Reference 6

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Observation ac57f7ba-1a63-45fd-98e1-20cab06a36e6 · inbound

Improving LLM Group Fairness on Tabular Data via In-Context Learning cites this paper.

Improving LLM Group Fairness on Tabular Data via In-Context Learning Unleashing the potential of prompt engineering for large language models

Reference 18

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Generative AI Literacy: Twelve Defining Competencies cites this paper.

Generative AI Literacy: Twelve Defining Competencies Unleashing the potential of prompt engineering for large language models

Reference 21

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Tree-of-Code: A Hybrid Approach for Robust Complex Task Planning and Execution cites this paper.

Tree-of-Code: A Hybrid Approach for Robust Complex Task Planning and Execution Unleashing the potential of prompt engineering for large language models

Reference 5

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Tree-of-Code: A Tree-Structured Exploring Framework for End-to-End Code Generation and Execution in Complex Task Handling cites this paper.

Tree-of-Code: A Tree-Structured Exploring Framework for End-to-End Code Generation and Execution in Complex Task Handling Unleashing the potential of prompt engineering for large language models

Reference 9

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Observation 250d77bf-f180-42e1-9b5a-47c60786f78e · inbound

Adaptive Few-shot Prompting for Machine Translation with Pre-trained Language Models cites this paper.

Adaptive Few-shot Prompting for Machine Translation with Pre-trained Language Models Unleashing the potential of prompt engineering for large language models

Reference 2023

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Observation 6fe7c2af-1b1f-4142-8d55-05f0dbd5efbf · inbound

Influences on LLM Calibration: A Study of Response Agreement, Loss Functions, and Prompt Styles cites this paper.

Influences on LLM Calibration: A Study of Response Agreement, Loss Functions, and Prompt Styles Unleashing the potential of prompt engineering for large language models

Reference 3

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Foundations of Large Language Models cites this paper.

Foundations of Large Language Models Unleashing the potential of prompt engineering for large language models

Reference 35

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Understanding the Effectiveness of LLMs in Automated Self-Admitted Technical Debt Repayment cites this paper.

Understanding the Effectiveness of LLMs in Automated Self-Admitted Technical Debt Repayment Unleashing the potential of prompt engineering for large language models

Reference 7

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Improved IR-based Bug Localization with Intelligent Relevance Feedback cites this paper.

Improved IR-based Bug Localization with Intelligent Relevance Feedback Unleashing the potential of prompt engineering for large language models

Reference 49

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LLM-Agents Driven Automated Simulation Testing and Analysis of small Uncrewed Aerial Systems cites this paper.

LLM-Agents Driven Automated Simulation Testing and Analysis of small Uncrewed Aerial Systems Unleashing the potential of prompt engineering for large language models

Reference 31

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Observation 00a95a42-92a5-4c7f-b23e-e34ac893ae7e · inbound

Code Change Intention, Development Artifact and History Vulnerability: Putting Them Together for Vulnerability Fix Detection by LLM cites this paper.

Code Change Intention, Development Artifact and History Vulnerability: Putting Them Together for Vulnerability Fix Detection by LLM Unleashing the potential of prompt engineering for large language models

Reference 7

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Text Data Augmentation for Large Language Models: A Comprehensive Survey of Methods, Challenges, and Opportunities cites this paper.

Text Data Augmentation for Large Language Models: A Comprehensive Survey of Methods, Challenges, and Opportunities Unleashing the potential of prompt engineering for large language models

Reference 87

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Observation 488fcc92-8dc3-4e1c-89ae-3a1a7ec94f47 · inbound

Training Users Against Human and GPT-4 Generated Social Engineering Attacks cites this paper.

Training Users Against Human and GPT-4 Generated Social Engineering Attacks Unleashing the potential of prompt engineering for large language models

Reference 12

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Observation 9c975aef-d918-4a8b-bab6-6a009f42388f · inbound

Developing an Artificial Intelligence Tool for Personalized Breast Cancer Treatment Plans based on the NCCN Guidelines cites this paper.

Developing an Artificial Intelligence Tool for Personalized Breast Cancer Treatment Plans based on the NCCN Guidelines Unleashing the potential of prompt engineering for large language models

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-10T22:09:29.480857Z

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Observation 7506e141-6003-4a13-84a1-362ca612649e · inbound

Trace-of-Thought Prompting: Investigating Prompt-Based Knowledge Distillation Through Question Decomposition cites this paper.

Trace-of-Thought Prompting: Investigating Prompt-Based Knowledge Distillation Through Question Decomposition Unleashing the potential of prompt engineering for large language models

Reference 4

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Does the Prompt-based Large Language Model Recognize Students' Demographics and Introduce Bias in Essay Scoring? cites this paper.

Does the Prompt-based Large Language Model Recognize Students' Demographics and Introduce Bias in Essay Scoring? Unleashing the potential of prompt engineering for large language models

Reference 3

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Enhancing the Learning Experience: Using Vision-Language Models to Generate Questions for Educational Videos cites this paper.

Enhancing the Learning Experience: Using Vision-Language Models to Generate Questions for Educational Videos Unleashing the potential of prompt engineering for large language models

Reference 8

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VISLIX: An XAI Framework for Validating Vision Models with Slice Discovery and Analysis cites this paper.

VISLIX: An XAI Framework for Validating Vision Models with Slice Discovery and Analysis Unleashing the potential of prompt engineering for large language models

Reference 15

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Observation 1fd8b627-82e9-4191-bfb7-0ee90d6d8dea · inbound

AI-Driven Scholarly Peer Review via Persistent Workflow Prompting, Meta-Prompting, and Meta-Reasoning cites this paper.

AI-Driven Scholarly Peer Review via Persistent Workflow Prompting, Meta-Prompting, and Meta-Reasoning Unleashing the potential of prompt engineering for large language models

Reference 28

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Validating the Effectiveness of a Large Language Model-based Approach for Identifying Children's Development across Various Free Play Settings in Kindergarten cites this paper.

Validating the Effectiveness of a Large Language Model-based Approach for Identifying Children's Development across Various Free Play Settings in Kindergarten Unleashing the potential of prompt engineering for large language models

Reference 39

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A Day in Their Shoes: Using LLM-Based Perspective-Taking Interactive Fiction to Reduce Stigma Toward Dirty Work cites this paper.

A Day in Their Shoes: Using LLM-Based Perspective-Taking Interactive Fiction to Reduce Stigma Toward Dirty Work Unleashing the potential of prompt engineering for large language models

Reference 16

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Observation 0800f009-6dd0-425c-89a8-ee02a5a21550 · inbound

Show or Tell? A Benchmark To Evaluate Visual and Textual Prompts in Semantic Segmentation cites this paper.

Show or Tell? A Benchmark To Evaluate Visual and Textual Prompts in Semantic Segmentation Unleashing the potential of prompt engineering for large language models

Reference 10

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Exploring Anthropomorphism in Conversational Agents for Environmental Sustainability cites this paper.

Exploring Anthropomorphism in Conversational Agents for Environmental Sustainability Unleashing the potential of prompt engineering for large language models

Reference 12

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BinMetric: A Comprehensive Binary Analysis Benchmark for Large Language Models cites this paper.

BinMetric: A Comprehensive Binary Analysis Benchmark for Large Language Models Unleashing the potential of prompt engineering for large language models

Reference 11

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Multilingual Prompt Engineering in Large Language Models: A Survey Across NLP Tasks cites this paper.

Multilingual Prompt Engineering in Large Language Models: A Survey Across NLP Tasks Unleashing the potential of prompt engineering for large language models

Reference 13

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Evolutionary Computation and Large Language Models: A Survey of Methods, Synergies, and Applications cites this paper.

Evolutionary Computation and Large Language Models: A Survey of Methods, Synergies, and Applications Unleashing the potential of prompt engineering for large language models

Reference 10

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Observation 521c8ab9-9271-41eb-a695-56b881badfd6 · inbound

Prompt Engineering: How Prompt Vocabulary affects Domain Knowledge cites this paper.

Prompt Engineering: How Prompt Vocabulary affects Domain Knowledge Unleashing the potential of prompt engineering for large language models

Reference 2005

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Extracting Research Instruments from Educational Literature Using LLMs cites this paper.

Extracting Research Instruments from Educational Literature Using LLMs Unleashing the potential of prompt engineering for large language models

Reference 12

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Privacy-preserving Prompt Personalization in Federated Learning for Multimodal Large Language Models cites this paper.

Privacy-preserving Prompt Personalization in Federated Learning for Multimodal Large Language Models Unleashing the potential of prompt engineering for large language models

Reference 6

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Observation d6da9cbe-cc77-4f30-b511-e0fead7168dc · inbound

From Parameters to Prompts: Understanding and Mitigating the Factuality Gap between Fine-Tuned LLMs cites this paper.

From Parameters to Prompts: Understanding and Mitigating the Factuality Gap between Fine-Tuned LLMs Unleashing the potential of prompt engineering for large language models

Reference 4

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Sword and Shield: Uses and Strategies of LLMs in Navigating Disinformation cites this paper.

Sword and Shield: Uses and Strategies of LLMs in Navigating Disinformation Unleashing the potential of prompt engineering for large language models

Reference 16

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FAA Framework: A Large Language Model-Based Approach for Credit Card Fraud Investigations cites this paper.

FAA Framework: A Large Language Model-Based Approach for Credit Card Fraud Investigations Unleashing the potential of prompt engineering for large language models

Reference 40

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Designing Effective LLM-Assisted Interfaces for Curriculum Development cites this paper.

Designing Effective LLM-Assisted Interfaces for Curriculum Development Unleashing the potential of prompt engineering for large language models

Reference 4

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AI-Facilitated Analysis of Abstracts and Conclusions: Flagging Unsubstantiated Claims and Ambiguous Pronouns cites this paper.

AI-Facilitated Analysis of Abstracts and Conclusions: Flagging Unsubstantiated Claims and Ambiguous Pronouns Unleashing the potential of prompt engineering for large language models

Reference 14

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Observation b400d99b-5beb-45a9-9e26-f70beaddeaa9 · inbound

Controlling Context: Generative AI at Work in Integrated Circuit Design and Other High-Precision Domains cites this paper.

Controlling Context: Generative AI at Work in Integrated Circuit Design and Other High-Precision Domains Unleashing the potential of prompt engineering for large language models

Reference 77

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Observation 467dc43d-428d-4010-b112-11a64ed6866d · inbound

Can GPT-4o Evaluate Usability Like Human Experts? A Comparative Study on Issue Identification in Heuristic Evaluation cites this paper.

Can GPT-4o Evaluate Usability Like Human Experts? A Comparative Study on Issue Identification in Heuristic Evaluation Unleashing the potential of prompt engineering for large language models

Reference 7

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Observation c5abbe50-7a07-47fa-8f73-fd0da8ddab65 · inbound

The Evolution of Natural Language Processing: How Prompt Optimization and Language Models are Shaping the Future cites this paper.

The Evolution of Natural Language Processing: How Prompt Optimization and Language Models are Shaping the Future Unleashing the potential of prompt engineering for large language models

Reference 14

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Observation 68dec1d7-e9d3-4631-b487-2ffd602b4a66 · inbound

Evaluating and Improving Large Language Models for Competitive Program Generation cites this paper.

Evaluating and Improving Large Language Models for Competitive Program Generation Unleashing the potential of prompt engineering for large language models

Reference 27

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Enhancing COBOL Code Explanations: A Multi-Agents Approach Using Large Language Models cites this paper.

Enhancing COBOL Code Explanations: A Multi-Agents Approach Using Large Language Models Unleashing the potential of prompt engineering for large language models

Reference 12

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LLM-based Question-Answer Framework for Sensor-driven HVAC System Interaction cites this paper.

LLM-based Question-Answer Framework for Sensor-driven HVAC System Interaction Unleashing the potential of prompt engineering for large language models

Reference 6

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An Evaluation of Large Language Models on Text Summarization Tasks Using Prompt Engineering Techniques cites this paper.

An Evaluation of Large Language Models on Text Summarization Tasks Using Prompt Engineering Techniques Unleashing the potential of prompt engineering for large language models

Reference 25

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Observation c8c796cc-e5f8-4a47-b86e-e6ecee701dcf · inbound

An Integrated Framework of Prompt Engineering and Multidimensional Knowledge Graphs for Legal Dispute Analysis cites this paper.

An Integrated Framework of Prompt Engineering and Multidimensional Knowledge Graphs for Legal Dispute Analysis Unleashing the potential of prompt engineering for large language models

Reference 21

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Observation 849ddd32-fa9e-479d-9be5-a641bbab57a9 · inbound

Promptomatix: An Automatic Prompt Optimization Framework for Large Language Models cites this paper.

Promptomatix: An Automatic Prompt Optimization Framework for Large Language Models Unleashing the potential of prompt engineering for large language models

Reference 3

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Observation efaac122-da3e-4583-a962-40f6e11a9e7c · inbound

From Prompt to Pipeline: Large Language Models for Scientific Workflow Development in Bioinformatics cites this paper.

From Prompt to Pipeline: Large Language Models for Scientific Workflow Development in Bioinformatics Unleashing the potential of prompt engineering for large language models

Reference 70

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Observation 068e527a-ee63-4e67-a3ba-ceccb67c9894 · inbound

CaTE Data Curation for Trustworthy AI cites this paper.

CaTE Data Curation for Trustworthy AI Unleashing the potential of prompt engineering for large language models

Reference 32

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Observation 864208de-1f96-4095-854d-3d27169f9c6e · inbound

CaTE Data Curation for Trustworthy AI cites this paper.

CaTE Data Curation for Trustworthy AI Unleashing the potential of prompt engineering for large language models

Reference 2024

Resolution: unresolved; no resolver link, observed 2026-08-05T18:23:21.858371Z

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Observation 9ab8cb29-450f-4460-9d6e-759f20f9b023 · inbound

Investigation of the Inter-Rater Reliability between Large Language Models and Human Raters in Qualitative Analysis cites this paper.

Investigation of the Inter-Rater Reliability between Large Language Models and Human Raters in Qualitative Analysis Unleashing the potential of prompt engineering for large language models

Reference 46

Resolution: unresolved; no resolver link, observed 2026-08-05T18:22:38.853411Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f341147c-ce98-4819-b3f0-08e6ef2fdb7f · inbound

Using an LLM to Investigate Students' Explanations on Conceptual Physics Questions cites this paper.

Using an LLM to Investigate Students' Explanations on Conceptual Physics Questions Unleashing the potential of prompt engineering for large language models

Reference 30

Resolution: verified exact; arxiv_id, observed 2026-05-18T22:06:52.022419Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 1677dc1e-aa1b-44ae-995d-32f8676a436b · inbound

Using LLMs to create analytical datasets: A case study of reconstructing the historical memory of Colombia cites this paper.

Using LLMs to create analytical datasets: A case study of reconstructing the historical memory of Colombia Unleashing the potential of prompt engineering for large language models

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-05T11:02:53.339463Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 42249d02-f410-4c20-a47f-dca93dd21728 · inbound

Large Language Models for Next-Generation Wireless Network Management: A Survey and Tutorial cites this paper.

Large Language Models for Next-Generation Wireless Network Management: A Survey and Tutorial Unleashing the potential of prompt engineering for large language models

Reference 171

Resolution: unresolved; no resolver link, observed 2026-08-05T04:50:32.154805Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 050c9420-fb39-4d51-8d9a-30a7235816ad · inbound

PIAST: Rapid Prompting with In-context Augmentation for Scarce Training data cites this paper.

PIAST: Rapid Prompting with In-context Augmentation for Scarce Training data Unleashing the potential of prompt engineering for large language models

Reference 23

Resolution: verified exact; arxiv_id, observed 2026-05-16T23:18:40.058929Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation f7ab7f8a-1aa9-4df0-9ad8-c4041a6dd421 · inbound

Prompts Blend Requirements and Solutions: From Intent to Implementation cites this paper.

Prompts Blend Requirements and Solutions: From Intent to Implementation Unleashing the potential of prompt engineering for large language models

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-02T18:07:18.954923Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 622a3c62-0e37-4e0d-a139-e17790836931 · inbound

Benchmarking LLM-Based Static Analysis for Secure Smart Contract Development: Reliability, Limitations, and Potential Hybrid Solutions cites this paper.

Benchmarking LLM-Based Static Analysis for Secure Smart Contract Development: Reliability, Limitations, and Potential Hybrid Solutions Unleashing the potential of prompt engineering for large language models

Reference 7

Resolution: verified exact; arxiv_id, observed 2026-05-13T02:22:06.988579Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation b58ea4d6-4122-4ced-b4da-1c267018971b · inbound

PromptAudit: Auditing Prompt Sensitivity in LLM-Based Vulnerability Detection cites this paper.

PromptAudit: Auditing Prompt Sensitivity in LLM-Based Vulnerability Detection Unleashing the potential of prompt engineering for large language models

Reference 9

Resolution: verified exact; arxiv_id, observed 2026-06-30T16:44:56.332416Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 474b604f-8ab2-42b7-9976-5d1dc725f0d3 · inbound

Enhancing Reliability in LLM-Based Secure Code Generation cites this paper.

Enhancing Reliability in LLM-Based Secure Code Generation Unleashing the potential of prompt engineering for large language models

Reference 27

Resolution: metadata mismatch; arxiv_id, observed 2026-06-30T15:14:46.058532Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 3a8c28ac-c9c1-4d72-907d-2e3d69f02045 · inbound

IstGPT: LLM-based Anomaly Detection for Spatial-Temporal Graph in Industrial Systems cites this paper.

IstGPT: LLM-based Anomaly Detection for Spatial-Temporal Graph in Industrial Systems Unleashing the potential of prompt engineering for large language models

Reference 55

Resolution: metadata mismatch; arxiv_id, observed 2026-07-01T23:26:22.001615Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 4d07c1b1-57d4-4e49-9e23-91428cc80578 · inbound

Wait, am I Being Fair? Characterizing Deductive Stereotyping and Mitigating It with Fair-GCG cites this paper.

Wait, am I Being Fair? Characterizing Deductive Stereotyping and Mitigating It with Fair-GCG Unleashing the potential of prompt engineering for large language models

Reference 77

Resolution: verified exact; arxiv_id, observed 2026-07-01T01:15:13.264009Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 2316d6d9-0949-4fd3-a476-0dc9f07ec03a · inbound

Prompt engineering using order-of-addition experiments: An application to generating two-level fractional factorial designs cites this paper.

Prompt engineering using order-of-addition experiments: An application to generating two-level fractional factorial designs Unleashing the potential of prompt engineering for large language models

Reference 63

Resolution: unresolved; no resolver link, observed 2026-07-11T06:09:35.110633Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 667f6769-3337-4e71-96be-ae554a9f108e · inbound

Transforming Remanufacturing Automation with Large Language Models: A Forward-Looking Analysis with Case Studies cites this paper.

Transforming Remanufacturing Automation with Large Language Models: A Forward-Looking Analysis with Case Studies Unleashing the potential of prompt engineering for large language models

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-06T14:54:47.251635Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 70b3a75a-4a82-4920-961d-9423d7ef6637 · inbound

Using LLMs to Detect Growth in Computational Thinking in Introductory Physics cites this paper.

Using LLMs to Detect Growth in Computational Thinking in Introductory Physics Unleashing the potential of prompt engineering for large language models

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-07T12:48:59.677280Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Coverage vector

Reference resolution

Outbound references

Pith citing papers