  The recent trend in the Large Vision and Language model has brought a new
change in how information extraction systems are built. VLMs have set a new
benchmark with their State-of-the-art techniques in understanding documents and
building question-answering systems across various industries. They are
significantly better at generating text from document images and providing
accurate answers to questions. However, there are still some challenges in
effectively utilizing these models to build a precise conversational system.
General prompting techniques used with large language models are often not
suitable for these specially designed vision language models. The output
generated by such generic input prompts is ordinary and may contain information
gaps when compared with the actual content of the document. To obtain more
accurate and specific answers, a well-targeted prompt is required by the vision
language model, along with the document image. In this paper, a technique is
discussed called Target prompting, which focuses on explicitly targeting parts
of document images and generating related answers from those specific regions
only. The paper also covers the evaluation of response for each prompting
technique using different user queries and input prompts.
