mlsanityData to 7 Oct 2026

A Systematic Survey of Prompt Engineering in Large Language Models: Techniques and Applications

arXiv 2402.07927 ↗First posted 5 Feb 2024cs.AI cs.CL cs.HCcited by 428

On the map: Multimodal LLMs / vision-language models → Prompt learning and adaptation of CLIP

Abstract (arXiv)

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.

Key facts. Cited by 428 papers in the map (data through 7 Oct 2026). Placed in Prompt learning and adaptation of CLIP, one of 16 directions in Multimodal LLMs / vision-language models. Builds on 32 of its 46 references that are in the map. First posted on arXiv 5 Feb 2024.

Builds on

32 of 46 references.

References · 32

  1. 2020Language Models are Few-Shot Learnerscited by 32,199
  2. 2022Chain-of-Thought Prompting Elicits Reasoning in Large Language Modelscited by 12,302
  3. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,160
  4. 2022ReAct: Synergizing Reasoning and Acting in Language Modelscited by 8,355
  5. 2022Self-Consistency Improves Chain of Thought Reasoning in Language Modelscited by 5,601
  6. 2023Tree of Thoughts: Deliberate Problem Solving with Large Language Modelscited by 3,155
  7. 2021Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processingcited by 2,222
  8. 2022Program of Thoughts Prompting: Disentangling Computation from Reasoning for Numerical Reasoning Taskscited by 1,057
  9. 2022Large Language Models Are Human-Level Prompt Engineerscited by 1,020
  10. 2021Show Your Work: Scratchpads for Intermediate Computation with Language Modelscited by 871
  11. 2023Large Language Models as Optimizerscited by 830
  12. 2022Automatic Chain of Thought Prompting in Large Language Modelscited by 684
20 more
  1. 2023Chain-of-Verification Reduces Hallucination in Large Language Modelscited by 317
  2. 2022Exploring Visual Prompts for Adapting Large-Scale Modelscited by 271
  3. 2024A Comprehensive Survey of Hallucination Mitigation Techniques in Large Language Modelscited by 266
  4. 2023Large Language Model Guided Tree-of-Thoughtcited by 218
  5. 2023ART: Automatic multi-step reasoning and tool-use for large language modelscited by 193
  6. 2024Chain-of-Table: Evolving Tables in the Reasoning Chain for Table Understandingcited by 185
  7. 2023Chain-of-Note: Enhancing Robustness in Retrieval-Augmented Language Modelscited by 180
  8. 2023Take a Step Back: Evoking Reasoning via Abstraction in Large Language Modelscited by 157
  9. 2023Active Prompting with Chain-of-Thought for Large Language Modelscited by 154
  10. 2023Large Language Models Understand and Can be Enhanced by Emotional Stimulicited by 143
  11. 2023Structured Chain-of-Thought Prompting for Code Generationcited by 136
  12. 2023Rephrase and Respond: Let Large Language Models Ask Better Questions for Themselvescited by 123
  13. 2023Chain-of-Knowledge: Grounding Large Language Models via Dynamic Knowledge Adapting over Heterogeneous Sourcescited by 121
  14. 2023Chain of Code: Reasoning with a Language Model-Augmented Code Emulatorcited by 120
  15. 2023System 2 Attention (is something you might need too)cited by 85
  16. 2023Thread of Thought Unraveling Chaotic Contextscited by 71
  17. 2023Contrastive Chain-of-Thought Promptingcited by 52
  18. 2023Enhancing Zero-Shot Chain-of-Thought Reasoning in Large Language Models through Logiccited by 34
  19. 2024Code Prompting Elicits Conditional Reasoning Abilities in Text+Code LLMscited by 19
  20. 2023EchoPrompt: Instructing the Model to Rephrase Queries for Improved In-context Learningcited by 13

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 105; 2025: 219; 2026: 104.

428 papers.

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By area:

  1. 2024LLMs-as-Judges: A Comprehensive Survey on LLM-based Evaluation Methodscited by 451
  2. 2024Multi-Step Reasoning with Large Language Models, a Surveycited by 140
  3. 2025A Survey of Context Engineering for Large Language Modelscited by 120
  4. 2024Next-Generation Database Interfaces: A Survey of LLM-based Text-to-SQLcited by 118
  5. 2024PhyT2V: LLM-Guided Iterative Self-Refinement for Physics-Grounded Text-to-Video Generationcited by 77
  6. 2024When LLMs Meet Cybersecurity: A Systematic Literature Reviewcited by 70
  7. 2025Effectively Controlling Reasoning Models through Thinking Interventioncited by 63
  8. 2025Large language models for automated scholarly paper review: A surveycited by 59
  9. 2025FLEX: Continuous Agent Evolution via Forward Learning from Experiencecited by 51
  10. 2026SkillNet: Create, Evaluate, and Connect AI Skillscited by 48
  11. 2024A Survey of Prompt Engineering Methods in Large Language Models for Different NLP Taskscited by 46
  12. 2025Large Language Models Hallucination: A Comprehensive Surveycited by 45
38 more
  1. 2024HelloBench: Evaluating Long Text Generation Capabilities of Large Language Modelscited by 42
  2. 2025Foundation Models in Autonomous Driving: A Survey on Scenario Generation and Scenario Analysiscited by 37
  3. 2025Navigate the Unknown: Enhancing LLM Reasoning with Intrinsic Motivation Guided Explorationcited by 33
  4. 2024CoMM: Collaborative Multi-Agent, Multi-Reasoning-Path Prompting for Complex Problem Solvingcited by 31
  5. 2024ClinicalBench: Can LLMs Beat Traditional ML Models in Clinical Prediction?cited by 31
  6. 2024Causal Prompting: Debiasing Large Language Model Prompting based on Front-Door Adjustmentcited by 29
  7. 2024Creativity Has Left the Chat: The Price of Debiasing Language Modelscited by 29
  8. 2024Meta Reasoning for Large Language Modelscited by 29
  9. 2024Heuristic-Induced Multimodal Risk Distribution Jailbreak Attack for Multimodal Large Language Modelscited by 29
  10. 2025Policy-as-Prompt: Rethinking Content Moderation in the Age of Large Language Modelscited by 29
  11. 2024Teach Better or Show Smarter? On Instructions and Exemplars in Automatic Prompt Optimizationcited by 28
  12. 2024Unveiling the Statistical Foundations of Chain-of-Thought Prompting Methodscited by 27
  13. 2025Belief Dynamics Reveal the Dual Nature of In-Context Learning and Activation Steeringcited by 24
  14. 2024iLLM-TSC: Integration reinforcement learning and large language model for traffic signal control policy improvementcited by 23
  15. 2024Attention Prompting on Image for Large Vision-Language Modelscited by 23
  16. 2025EgoExoBench: A Benchmark for First- and Third-person View Video Understanding in MLLMscited by 22
  17. 2026CooperBench: Why Coding Agents Cannot be Your Teammates Yetcited by 20
  18. 2024PaCE: Parsimonious Concept Engineering for Large Language Modelscited by 19
  19. 2024Large Language Models for Constructing and Optimizing Machine Learning Workflows: A Surveycited by 19
  20. 2025HeuriGym: An Agentic Benchmark for LLM-Crafted Heuristics in Combinatorial Optimizationcited by 19
  21. 2025Adaptation of Agentic AI: A Survey of Post-Training, Memory, and Skillscited by 19
  22. 2026VideoAuto-R1: Video Auto Reasoning via Thinking Once, Answering Twicecited by 19
  23. 2024Prompt-SAW: Leveraging Relation-Aware Graphs for Textual Prompt Compressioncited by 18
  24. 2024ALPS: Improved Optimization for Highly Sparse One-Shot Pruning for Large Language Modelscited by 18
  25. 2025Representation Engineering for Large-Language Models: Survey and Research Challengescited by 18
  26. 2025A Survey of AIOps in the Era of Large Language Modelscited by 18
  27. 2024On the Brittle Foundations of ReAct Prompting for Agentic Large Language Modelscited by 17
  28. 2025Parallels Between VLA Model Post-Training and Human Motor Learning: Progress, Challenges, and Trendscited by 17
  29. 2024Iteration of Thought: Leveraging Inner Dialogue for Autonomous Large Language Model Reasoningcited by 16
  30. 2025HyperFree: A Channel-adaptive and Tuning-free Foundation Model for Hyperspectral Remote Sensing Imagerycited by 16
  31. 2026On the Impact of AGENTS.md Files on the Efficiency of AI Coding Agentscited by 16
  32. 2025Large Language Model (LLM) for Software Security: Code Analysis, Malware Analysis, Reverse Engineeringcited by 15
  33. 2025AlphaAgents: Large Language Model based Multi-Agents for Equity Portfolio Constructionscited by 15
  34. 2024EPIC: Effective Prompting for Imbalanced-Class Data Synthesis in Tabular Data Classification via Large Language Modelscited by 14
  35. 2024ChatGPT Meets Iris Biometricscited by 14
  36. 2024Next-Generation Phishing: How LLM Agents Empower Cyber Attackerscited by 14
  37. 2024CATP-LLM: Empowering Large Language Models for Cost-Aware Tool Planningcited by 14
  38. 2025PluralLLM: Pluralistic Alignment in LLMs via Federated Learningcited by 14

50 most cited of 428 All 428 (JSON)

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  1. 2023A Systematic Survey of Prompt Engineering on Vision-Language Foundation Models
  2. 2024Efficient Prompting Methods for Large Language Models: A Survey
  3. 2023Unleashing the potential of prompt engineering for large language models
  4. 2025The Future of MLLM Prompting is Adaptive: A Comprehensive Experimental Evaluation of Prompt Engineering Methods for Robust Multimodal Performance
  5. 2024Towards Generalist Prompting for Large Language Models by Mental Models
  6. 2025Modeling Variants of Prompts for Vision-Language Models
  7. 2026The PICCO Framework for Large Language Model Prompting: A Taxonomy and Reference Architecture for Prompt Structure
  8. 2024Learning to Prompt with Text Only Supervision for Vision-Language Models