Crafting precise, academically rigorous abstracts for scientific articles demands more than summarising content—it requires clarity, accuracy, and careful adherence to scholarly conventions. This blog introduces a structured prompt tailored to guide AI models in producing high-quality, publication-ready abstracts for English-language research articles. When crafting your prompt for AI-assisted abstract generation, ensure it explicitly instructs the model to include the research objectives, methodology, key findings, and both theoretical and practical implications.
Prompt
Abstract Generation Task
You are an AI expert in generating concise and academically rigorous abstracts for scientific research articles. Your task is to summarise the uploaded research paper clearly and accurately in UK English. The resulting abstract (150–200 words) should capture the study’s objectives, methodology, key findings, and broader implications.
Instructions:
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Clearly identify the study’s main components:
- Explicitly state the research objective or problem.
- Summarise the methodology, including data sources and analytical approach.
- Highlight key findings and their significance.
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Maintain a formal, clear, and structured approach:
- Ensure coherence and clarity, avoiding unnecessary complexity.
- Use precise academic language; avoid colloquialisms.
- Adhere strictly to UK spelling and style conventions.
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Ensure the abstract is balanced:
- Provide sufficient detail without excessive elaboration.
- Clearly outline both theoretical and practical contributions.
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The abstract must:
- Be between 150–200 words.
- Have a neutral, objective, and scholarly tone.
- Be suitable for publication in an academic journal.
- Reapply these instructions consistently during abstract generation.
Abstract structure example:
- Objective: Clearly state the research aim or problem.
- Methodology: Summarise data sources, research design, and analytical methods.
- Key Findings: Highlight essential results and their academic relevance.
- Implications: Briefly outline theoretical and practical implications or recommendations arising from the findings.
Input File
We tested the prompt using Claude 3.7 Sonnet. The input was a PDF version of an English-language scientific research article introducing HUNCOURT, an open-access legal database presented in this published paper. To ensure the AI-generated abstract was unbiased, we removed the original abstract by cropping out the document's first page. We also excluded the appendix.
The cropped PDF used as input is available here:
Output
Recommendation
The test conducted with Claude 3.7 Sonnet demonstrated the model’s ability to generate a concise, structured, and academically rigorous abstract, effectively encapsulating the core elements of the original article. The AI-generated abstract successfully highlighted the objectives, methodological strengths, and wider scholarly contributions of the HUNCOURT database. This outcome underscores the potential of carefully designed prompts to facilitate accurate and publication-ready abstract generation, particularly for complex fields such as legal informatics, where systematic quantitative analysis can significantly enhance existing qualitative research approaches.
The authors used Claude 3.7 Sonnet [Anthropic (2025) Claude 3.7 Sonnet (accessed on 28 February 2025), Large language model (LLM), available at: https://www.anthropic.com] to generate the output.
