docs: add findings and future work section

intro.md

@@ -25,6 +25,20 @@ We present three demos, which each illustrate different use cases of KoCLIP.
 * *Text to * Image*: This is essentially an image retrieval task. Given a text, the model looks up a database of pre-computed image embeddings to retrive the image that best matches given text. 
 * *Text to Patch*: This is also a variant of zero-shot image classification. Given a text and an image, the image is partitioned into subsections, and the model ranks them based on their relevance with the text query.
 
+## Prompting
+
+We found that KoCLIP performs better when prompting is used to induce zero-shot behavior. Namely, instead of feeding it a single word or short phrase, casting a template such as
+
+```
+이것은 {{}} 이다 (This is {{}}.)
+```
+
+noticably helped the model. We hypothesize that this is due to the nature of captions in the MSCOCO datset, which are most often full sentences, albeit sometimes short in length.  
+
+## Future Work
+
+Due to time and resource contraints, we have yet to compare KoCLIP to other open-source baselines, such as [M-CLIP](https://huggingface.co/M-CLIP). We hope to benchmark KoCLIP on various metrics and evaluation datasets to further determine its performance and reliability. In addition, given that prompting is somewhat of a mysterious trick and an active area of ongoing research, we hope to explore ways to take a more scientific approach on prompt engineering.
+
 ---
 
 We thank the teams at Hugging Face and Google for arranging this wonderful oportunity. It has been a busy yet enormously rewarding week for all of us. Hope you enjoy the demo!