Context-Aware Systems to AI Prompt Engineering:
A Conceptual Framework for Context Modeling in Translation Studies
Abstract
With the growing use of large language models in translation, the quality of their outputs increasingly depends on how contextual information is modeled and presented in prompts. However, selecting and organizing such information still relies mainly on user experience and trial-and-error approaches, while no theoretical framework exists for systematic context modeling in interaction with these models. To address this gap, this study proposes a conceptual framework for context modeling in translation studies by drawing on concepts from context-aware systems and reinterpreting them in the context of prompt engineering. The study is fundamental and qualitative, based on a comparative review and analysis of literature across three domains: translation studies, context-aware systems, and prompt engineering. Based on the literature analysis, a framework comprising six layers—linguistic, discourse, cultural, audience, translation task, and interaction with the language model—was designed. To preliminarily examine its practical applicability, translations of two selected examples were compared using a zero-shot baseline and six cumulative configurations based on the proposed layers. The findings indicate that the proposed framework can bridge the theoretical foundations of translation studies, context modeling in software engineering, and prompt engineering, providing a basis for future empirical research.
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Copyright (c) 2026 Hamed Barangi, Ebrahim Ezzati
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Copyright Licensee: Iranian Journal of Translation Studies. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution–NonCommercial 4.0 International (CC BY-NC 4.0 license).
