README.md

Prompt Engineering Techniques for Finance 👨‍💻📝

Leverage prompt engineering techniques to curate prompts by using Large Language Model's language understanding and generation in the space of Finance. Run through the notebook within Google Colab and have fun! 💯

FAQ

Which LLM is being used here?

Largely experiemented with LLAMA3-70B amongst the available LLMs in GROQ API due to its superiority in performance. You are free to explore other available models as well!

What are the use-cases identified here?

(1) Automated Analysis of Stock Performance

(2) Financial Data Extraction from Earnings Call Transcript

(3) Sentiment Analysis of FED Chair FOMC on Interest Rate Decision

What are some of the prompt engineering techniques explored in this notebok?

(1) Few Shot Prompting 🚀🚀🚀

(2) Zero Shot Prompting 🚀

You can also explore others such as Chain-of-Thought Prompting, Plan-and-solve prompting, etc.

API Reference

Get all items

Parameter	Type	Description
GROQ_API_KEY	string	Required. Your GROQ API key put into Colab Secrets

Authors

• @sciencenerd880

👊References

• GROQs API - LLMs Comparison
• Leaderboard LLMs