Impromptu: a framework for model-driven prompt engineering
Sergio Morales, Robert Clarisó, Jordi Cabot
Abstract
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 , a model-driven engineering framework to support the creation, management and reuse of prompts for generative AI. 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. is available as a Langium-based Visual Studio Code plugin.