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The Prompt Engineering Report Distilled: Quick Start Guide for Life Sciences

arXiv 2509.11295 ↗First posted 14 Sep 2025cs.CLcited by 0

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Abstract (arXiv)

Developing effective prompts demands significant cognitive investment to generate reliable, high-quality responses from Large Language Models (LLMs). By deploying case-specific prompt engineering techniques that streamline frequently performed life sciences workflows, researchers could achieve substantial efficiency gains that far exceed the initial time investment required to master these techniques. The Prompt Report published in 2025 outlined 58 different text-based prompt engineering techniques, highlighting the numerous ways prompts could be constructed. To provide actionable guidelines and reduce the friction of navigating these various approaches, we distil this report to focus on 6 core techniques: zero-shot, few-shot approaches, thought generation, ensembling, self-criticism, and decomposition. We breakdown the significance of each approach and ground it in use cases relevant to life sciences, from literature summarization and data extraction to editorial tasks. We provide detailed recommendations for how prompts should and shouldn't be structured, addressing common pitfalls including multi-turn conversation degradation, hallucinations, and distinctions between reasoning and non-reasoning models. We examine context window limitations, agentic tools like Claude Code, while analyzing the effectiveness of Deep Research tools across OpenAI, Google, Anthropic and Perplexity platforms, discussing current limitations. We demonstrate how prompt engineering can augment rather than replace existing established individual practices around data processing and document editing. Our aim is to provide actionable guidance on core prompt engineering principles, and to facilitate the transition from opportunistic prompting to an effective, low-friction systematic practice that contributes to higher quality research.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 38 of its 65 references that are in the map. First posted on arXiv 14 Sep 2025.

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38 of 65 references.

References · 38

  1. 2020Language Models are Few-Shot Learnerscited by 32,419
  2. 2022Chain-of-Thought Prompting Elicits Reasoning in Large Language Modelscited by 12,459
  3. 2022Self-Consistency Improves Chain of Thought Reasoning in Language Modelscited by 5,708
  4. 2023Reflexion: Language Agents with Verbal Reinforcement Learningcited by 4,276
  5. 2021On the Opportunities and Risks of Foundation Modelscited by 4,033
  6. 2021Finetuned Language Models Are Zero-Shot Learnerscited by 3,780
  7. 2023Self-Refine: Iterative Refinement with Self-Feedbackcited by 3,442
  8. 2022Least-to-Most Prompting Enables Complex Reasoning in Large Language Modelscited by 1,407
  9. 2021What Makes Good In-Context Examples for GPT-3?cited by 1,195
  10. 2022Large Language Models Can Self-Improvecited by 664
  11. 2023Plan-and-Solve Prompting: Improving Zero-Shot Chain-of-Thought Reasoning by Large Language Modelscited by 560
  12. 2025LLMs Get Lost In Multi-Turn Conversationcited by 429
26 more
  1. 2025DeepResearch Bench: A Comprehensive Benchmark for Deep Research Agentscited by 248
  2. 2023Adapted Large Language Models Can Outperform Medical Experts in Clinical Text Summarizationcited by 246
  3. 2022Selective Annotation Makes Language Models Better Few-Shot Learnerscited by 243
  4. 2024LLM Maybe LongLM: Self-Extend LLM Context Window Without Tuningcited by 136
  5. 2024The Prompt Report: A Systematic Survey of Prompt Engineering Techniquescited by 121
  6. 2024Make Your LLM Fully Utilize the Contextcited by 119
  7. 2022Ground-Truth Labels Matter: A Deeper Look into Input-Label Demonstrationscited by 116
  8. 2025Confidence Improves Self-Consistency in LLMscited by 113
  9. 2025LLM Generated Persona is a Promise with a Catchcited by 81
  10. 2024Delving into LLM-assisted writing in biomedical publications through excess vocabularycited by 78
  11. 2023Igniting Language Intelligence: The Hitchhiker's Guide From Chain-of-Thought Reasoning to Language Agentscited by 72
  12. 2025WideSearch: Benchmarking Agentic Broad Info-Seekingcited by 69
  13. 2023ChatGPT Chemistry Assistant for Text Mining and Prediction of MOF Synthesiscited by 51
  14. 2024LLM-Ensemble: Optimal Large Language Model Ensemble Method for E-commerce Product Attribute Value Extractioncited by 39
  15. 2024Toward a Theory of Tokenization in LLMscited by 30
  16. 2025Generalization Bias in Large Language Model Summarization of Scientific Researchcited by 30
  17. 2025Navigating Rifts in Human-LLM Grounding: Study and Benchmarkcited by 29
  18. 2024Large Language Models in Drug Discovery and Development: From Disease Mechanisms to Clinical Trialscited by 24
  19. 2025Stay Focused: Problem Drift in Multi-Agent Debatecited by 23
  20. 2024Helpful assistant or fruitful facilitator? Investigating how personas affect language model behaviorcited by 21
  21. 2024Chain-of-Thought Prompting for Speech Translationcited by 17
  22. 2024ProLLM: Protein Chain-of-Thoughts Enhanced LLM for Protein-Protein Interaction Predictioncited by 12
  23. 2024Automated, LLM enabled extraction of synthesis details for reticular materials from scientific literaturecited by 7
  24. 2025Token-by-Token Regeneration and Domain Biases: A Benchmark of LLMs on Advanced Mathematical Problem-Solvingcited by 2
  25. 2025GPT Editors, Not Authors: The Stylistic Footprint of LLMs in Academic Preprintscited by 2
  26. 2025Reliable Decision Support with LLMs: A Framework for Evaluating Consistency in Binary Text Classification Applicationscited by 1

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  1. 2025Understanding LLM Scientific Reasoning through Promptings and Model's Explanation on the Answers
  2. 2024From Medprompt to o1: Exploration of Run-Time Strategies for Medical Challenge Problems and Beyond
  3. 2023An Empirical Evaluation of Prompting Strategies for Large Language Models in Zero-Shot Clinical Natural Language Processing
  4. 2026Beyond the Answer: Decoding the Behavior of LLMs as Scientific Reasoners
  5. 2024A Systematic Survey of Prompt Engineering in Large Language Models: Techniques and Applications
  6. 2024The Prompt Canvas: A Literature-Based Practitioner Guide for Creating Effective Prompts in Large Language Models
  7. 2025Transforming Science with Large Language Models: A Survey on AI-assisted Scientific Discovery, Experimentation, Content Generation, and Evaluation
  8. 2025AI-Driven Scholarly Peer Review via Persistent Workflow Prompting, Meta-Prompting, and Meta-Reasoning