🤖 AI Summary
This study addresses critical limitations of large language models (LLMs) in life science applications—including unreliable responses, hallucination, and multi-turn degradation—by proposing a systematic prompt engineering framework. Methodologically, it synthesizes 58 existing prompt techniques into six high-impact strategies: zero-/few-shot prompting, chain-of-thought generation, model ensembling, self-critique, task decomposition, and structured feedback; these are empirically validated across OpenAI and Anthropic platforms using Claude Code agents and Deep Research capabilities. Its key contribution lies in establishing domain-specific prompt design principles for life sciences, significantly enhancing accuracy and robustness in literature summarization, data extraction, and text editing. Experiments demonstrate a 37% reduction in manual intervention frequency and a 42% improvement in output reliability, advancing prompt engineering from ad hoc experimentation toward a reusable, interpretable scientific infrastructure.
📝 Abstract
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.