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Use of Large Language Models For stock research



Being a person exposed to ai ,llms and investing due to my job and being a healthy Investors always fascinated me can we leveraged both.

In a cost effective and experimental way due to work I was exploring into RAG systems and after using it with investor conference material of navine flourine , I was truly amazed that it actually did something in reality as I put around 20+ conference documents.

So does anyone know or tried something like this as I want some inputs as this system that I am building is currently building is good to do many type of docs but have only started less then 2 week ago with some issues and mostly solved in coding process.

So would like to discuss with the community’s over it and if is fruitful would also love to share( note I have made as a side project but have also devoted some time of company so need to ask the job company before sharing as it was a part of their poc)

As a note this is my first post as a first thread so would also love some comments also how to write better



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Can you please explain, you put 20+ documents of conf calls, what prompt you gave & how it performed?

Like their is no limit of documents like I have put around 15+ docs though it took around.

For the case of prompt it was earlier designed to be a chat like model at least in terms of persona now I am switching to a financial analyst persona(testing is required).

In current scheme was using while con call but I am also testing summarization + embedding technique with a better model.

Like was using tinny llama but using mistral , phi or even llama 2 40B could yield better result.

Like as a question it was able to translate the growth and explain what molecules company make and some other like future capes.

But right now comparison don’t work.

I am also trying to find Claude 2 like large model so a great amount of data can be their in case of context



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