Evaluating the role of prompt engineering in improving AI-generated audio description for factual TV/media genres

Start date

2026

End date

Ongoing

About the project

This project investigates how far prompt engineering can improve AI-generated audio description (AD) for factual television content. Building on our previous evaluation that revealed both strong potential and persistent weaknesses in AI-generated AD, the project shifts attention to prompt design as a potentially fast, flexible and cost-effective way to improve output quality. The overarching aim is to determine whether adjustments in prompt phrasing, structure and stylistic guidance can increase the accuracy, relevance and narrative cohesion of AD produced by multimodal AI models capable of processing text, image, audio and video. 

Aims and objectives

To achieve this, the project will re-use the existing factual TV dataset, along with the baseline human and AI-generated AD from the previous project, enabling direct comparison across versions. Prompt-engineering strategies will be developed and systematically tested, with outputs recorded in a structured log to identify effective patterns. Linguistic evaluation will examine improvements, while a focus group with AD users and, where feasible, professional describers will assess perceptual and experiential gains. 

A new task will be added to the project: evaluating an existing AD platform to test the AI assistant integrated into its workflow. This assessment will examine how the platform’s built-in AI support performs on the project’s factual TV material and how this compares with outputs produced through the bespoke prompt-engineering strategies developed in the study. Findings from this platform evaluation will expand the scope of the linguistic analysis. The results will be reported alongside the comparative evaluation of baseline and newly generated AD versions.

For enquiries or potential collaboration on this topic please contact Prof Sabine Braun, the Principal Investigator of the project.

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