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A Survey of Automatic Prompt Engineering: An Optimization Perspective

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arxiv 2502.11560 v1 pith:IKVJJ4Y5 submitted 2025-02-17 cs.AI cs.LG

Wenwu Li , Xiangfeng Wang , Wenhao Li , Bo Jin This is my paper · ORCID
classification cs.AIcs.LG
keywords promptoptimizationengineeringmethodssurveyacrossautomateddesign
verification ladder T0 review T1 audit T2 compute T3 formal
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The rise of foundation models has shifted focus from resource-intensive fine-tuning to prompt engineering, a paradigm that steers model behavior through input design rather than weight updates. While manual prompt engineering faces limitations in scalability, adaptability, and cross-modal alignment, automated methods, spanning foundation model (FM) based optimization, evolutionary methods, gradient-based optimization, and reinforcement learning, offer promising solutions. Existing surveys, however, remain fragmented across modalities and methodologies. This paper presents the first comprehensive survey on automated prompt engineering through a unified optimization-theoretic lens. We formalize prompt optimization as a maximization problem over discrete, continuous, and hybrid prompt spaces, systematically organizing methods by their optimization variables (instructions, soft prompts, exemplars), task-specific objectives, and computational frameworks. By bridging theoretical formulation with practical implementations across text, vision, and multimodal domains, this survey establishes a foundational framework for both researchers and practitioners, while highlighting underexplored frontiers in constrained optimization and agent-oriented prompt design.

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Cited by 9 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. TAPR: Enhancing LLM Performance with a Task-Aware Prompt Rewriter

    cs.AI 2026-07 conditional novelty 6.0 of 10

    A small LLM trained with GRPO and LLM-judge rewards rewrites simple prompts into more effective ones, improving question-answering and arithmetic accuracy over base prompts while giving mixed, often negligible gains o...

  2. From Errors to Rules: Iterative Prompt Optimization for Text Classification

    cs.AI 2026-06 conditional novelty 6.0 of 10

    Error-driven prompt optimization (ERGO) beats demonstration and search methods on boundary-learnable tasks (TREC 90.0, CLINC150 94.4), but no paradigm dominates overall.

  3. FitText: Evolving Agent Tool Ecologies via Memetic Retrieval

    cs.AI 2026-05 unverdicted novelty 6.0 of 10

    FitText embeds memetic evolutionary retrieval inside the agent's reasoning loop to iteratively refine pseudo-tool descriptions, raising retrieval rank from 8.81 to 2.78 on ToolRet and pass rate to 0.73 on StableToolBench.

  4. APIO: Automatic Prompt Induction and Optimization for Grammatical Error Correction and Text Simplification

    cs.CL 2025-08 conditional novelty 6.0 of 10

    APIO automatically induces and optimizes instruction-list prompts for grammatical error correction and text simplification, reporting improved scores over prior prompt-based methods on BEA-2019 and ASSET.

  5. Stabilizing Black-Box Prompt Optimization with Textual Regularization and Signal Aggregation

    cs.LG 2025-07 conditional novelty 6.0 of 10

    TRAS adds success-based textual regularization and Monte Carlo signal aggregation to black-box prompt optimization, improving accuracy and reducing instruction loss when moving prompts across models.

  6. Towards Unveiling Vulnerabilities of Large Reasoning Models in Machine Unlearning

    cs.LG 2026-04 conditional novelty 5.0 of 10

    A practitioner methodology of role-ranked context packages and a four-stage pipeline is associated with better first-pass AI output quality in a single-operator observational study of 200 interactions.

  7. Parameter-Efficient Adaptation of mPLUG-Owl2 via Pixel-Level Visual Prompts for NR-IQA

    cs.CV 2025-09 conditional novelty 5.0 of 10

    With a learned 30-pixel border prompt added to input images, a frozen mPLUG-Owl2-7B reaches 0.932 SRCC on KADID-10k using about 156K trainable parameters.

  8. Towards Efficient and Robust Linguistic Emotion Diagnosis for Mental Health via Multi-Agent Instruction Refinement

    cs.AI 2026-01 reject novelty 4.0 of 10

    A multi-agent prompt-rewriting loop is claimed to improve LLM emotion diagnosis accuracy, but its evaluation appears to optimize on the test set and lacks replication details.

  9. Causal Prompting for Implicit Sentiment Analysis with Large Language Models

    cs.CL 2025-07 conditional novelty 4.0 of 10

    CAPITAL boosts implicit sentiment classification by weighting chain-of-thought outputs through a front-door adjustment approximation, but the causal mechanism is asserted rather than established.

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