A bit of context:
I’m working on a “Performance review assistant” as a way to understand the OpenAI capabilities. The capabilities of this assistant are to:
- synthesize feedback content from a large number of individuals - with a number of different relationships. For example, upward feedback to managers, peer feedback, manager feedback etc.
- summarize the feedback for the user. For example: “what are areas of improvement that engineers have identified for their managers?” or “What aspects of their jobs are support analysts most satisfied with?”
- generate content for the user based on specific frameworks. For example: “summarize the feedback to to the CFO using the start, stop, continue framework.”
Creating this assistant would allow me to:
- Learn how to provide the model with very specific domain information
- Understand how to leverage the underlying model’s language generation capabilities to operate on my domain specific data
- Apply my intuition to evaluate whether the responses from the model “sound right”.
What is the best way for me to supply the domain specific data? The best guidance I’ve seen on this so far is the following cook-book recommending embeddings over fine-tuning: openai-cookbook/Question_answering_using_embeddings.ipynb at main · openai/openai-cookbook · GitHub
I’m still early in my learning here, and wanted to clarify whether this approach would still allow me to use the Completions service to generate content, or whether I would be limited to the Answers service and simple retrieval applications.
Thanks in advance for any insight on this, crew!!