Summary

In this chapter, we explored metadata extraction from financial documents, specifically 10-K reports filed by publicly traded companies. We walked through the experience of working with a financial services firm that needs to extract key data points from these massive 10-K annual reports, leveraging the data extraction capabilities of LLMs.

We defined the use case, and we leveraged the power of GenAI to navigate through the structured sections of a 10-K, pinpointing and extracting the most relevant information nuggets, following the guidance provided by a best practices document. We walked through the process, starting by crafting an effective prompt to guide the AI model. This involved studying an SEC resource that outlines the critical sections and data points that investors should focus on. Armed with this knowledge, we can iteratively refine our prompts to ensure accurate and efficient extraction.

Then, we proposed a cloud-native, serverless architecture on Google...