View in EDS HTML Full Text PDF Full Text

Impromptu: a framework for model-driven prompt engineering.

Saved in:
Bibliographic Details
Title: Impromptu: a framework for model-driven prompt engineering.
Authors: Morales, Sergio1 (AUTHOR) smoralesg@uoc.edu, Clarisó, Robert1 (AUTHOR) rclariso@uoc.edu, Cabot, Jordi2,3 (AUTHOR) jordi.cabot@list.lu
Source: Software & Systems Modeling. Dec2025, Vol. 24 Issue 6, p1627-1645. 19p.
Subjects: Generative artificial intelligence, Domain-specific programming languages, Natural language processing, Software development tools, Model-driven software architecture, Artificial intelligence
Abstract: Generative artificial intelligence (AI) systems are capable of synthesizing complex artifacts such as text, source code or images according to the instructions provided in a natural language prompt. The quality of the input prompt, in terms of both content and structure, has a large impact on the quality of the output. This has given rise to prompt engineering, the process of designing natural language prompts to best take advantage of the capabilities of generative AI systems. This paper describes Impromptu , a model-driven engineering framework to support the creation, management and reuse of prompts for generative AI. Impromptu offers a domain-specific language (DSL) to define multimodal prompts in a modular and tool-independent way. The language offers additional features such as versioning, prompt chaining and multi-language support. Moreover, it provides tool support to adapt prompts for specific generative AI systems, execute those prompts on a generative AI system and validate the quality of the response that is generated. Impromptu is available as a Langium-based Visual Studio Code plugin. [ABSTRACT FROM AUTHOR]
Copyright of Software & Systems Modeling is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
Full text is not displayed to guests.
FullText Links:
  – Type: pdflink
Text:
  Availability: 1
Header DbId: egs
DbLabel: Engineering Source
An: 189358297
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
IllustrationInfo
Items – Name: Title
  Label: Title
  Group: Ti
  Data: Impromptu: a framework for model-driven prompt engineering.
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Morales%2C+Sergio%22">Morales, Sergio</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> smoralesg@uoc.edu</i><br /><searchLink fieldCode="AR" term="%22Clarisó%2C+Robert%22">Clarisó, Robert</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> rclariso@uoc.edu</i><br /><searchLink fieldCode="AR" term="%22Cabot%2C+Jordi%22">Cabot, Jordi</searchLink><relatesTo>2,3</relatesTo> (AUTHOR)<i> jordi.cabot@list.lu</i>
– Name: TitleSource
  Label: Source
  Group: Src
  Data: <searchLink fieldCode="JN" term="%22Software+%26+Systems+Modeling%22">Software & Systems Modeling</searchLink>. Dec2025, Vol. 24 Issue 6, p1627-1645. 19p.
– Name: Subject
  Label: Subjects
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22Generative+artificial+intelligence%22">Generative artificial intelligence</searchLink><br /><searchLink fieldCode="DE" term="%22Domain-specific+programming+languages%22">Domain-specific programming languages</searchLink><br /><searchLink fieldCode="DE" term="%22Natural+language+processing%22">Natural language processing</searchLink><br /><searchLink fieldCode="DE" term="%22Software+development+tools%22">Software development tools</searchLink><br /><searchLink fieldCode="DE" term="%22Model-driven+software+architecture%22">Model-driven software architecture</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+intelligence%22">Artificial intelligence</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Generative artificial intelligence (AI) systems are capable of synthesizing complex artifacts such as text, source code or images according to the instructions provided in a natural language prompt. The quality of the input prompt, in terms of both content and structure, has a large impact on the quality of the output. This has given rise to prompt engineering, the process of designing natural language prompts to best take advantage of the capabilities of generative AI systems. This paper describes Impromptu , a model-driven engineering framework to support the creation, management and reuse of prompts for generative AI. Impromptu offers a domain-specific language (DSL) to define multimodal prompts in a modular and tool-independent way. The language offers additional features such as versioning, prompt chaining and multi-language support. Moreover, it provides tool support to adapt prompts for specific generative AI systems, execute those prompts on a generative AI system and validate the quality of the response that is generated. Impromptu is available as a Langium-based Visual Studio Code plugin. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Software & Systems Modeling is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=egs&AN=189358297
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.1007/s10270-024-01235-4
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 19
        StartPage: 1627
    Subjects:
      – SubjectFull: Generative artificial intelligence
        Type: general
      – SubjectFull: Domain-specific programming languages
        Type: general
      – SubjectFull: Natural language processing
        Type: general
      – SubjectFull: Software development tools
        Type: general
      – SubjectFull: Model-driven software architecture
        Type: general
      – SubjectFull: Artificial intelligence
        Type: general
    Titles:
      – TitleFull: Impromptu: a framework for model-driven prompt engineering.
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: Morales, Sergio
      – PersonEntity:
          Name:
            NameFull: Clarisó, Robert
      – PersonEntity:
          Name:
            NameFull: Cabot, Jordi
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 01
              M: 12
              Text: Dec2025
              Type: published
              Y: 2025
          Identifiers:
            – Type: issn-print
              Value: 16191366
          Numbering:
            – Type: volume
              Value: 24
            – Type: issue
              Value: 6
          Titles:
            – TitleFull: Software & Systems Modeling
              Type: main
ResultId 1