I am currently developing a platform to help MBA candidates write unique MBA essays. I applied to HBS and INSEAD in the past, and when I used ChatGPT to assist me with the essays, the answers were really generic and the text used very complex words.
I have access to a database of essays from successful candidates and different universities, and I want to use this data to increase the quality of the outputs from GPT. I am just trying to decide if it's better to (1) leverage OpenAI assistants, feed the documents as knowledge and optimize prompts for each essay or (2) fine-tune a model such as GPT 3.5.
I have access to a database of essays from successful candidates and different universities, and I want to use this data to increase the quality of the outputs from GPT. I am just trying to decide if it's better to (1) leverage OpenAI assistants, feed the documents as knowledge and optimize prompts for each essay or (2) fine-tune a model such as GPT 3.5.
Any suggestions/recommendations?