Read Part 1 here. Introduction In the realm of financial analysis, structured data extraction from complex documents like SEC 10-Q filings can revolutionize how investors make decisions. The 10-Q Analyzer project leverages AI to automate this process, but like any technological solution, it comes with its own set of advantages, disadvantages, and challenges. This blog … Continue reading Building an AI 10-Q Analyzer: Part 2 | Navigating the Pros and Cons of Structured Output from 10-Q Systems
Tag: RAG
Building a 10-Q Analyzer: Part 1 | Extracting Financial Insights with AI
Read Part 2 and Part 3 In the evolving landscape of artificial intelligence, combining advanced techniques like Retrieval-Augmented Generation (RAG) and Named Entity Recognition (NER) has opened new avenues for extracting and structuring information from complex documents. This blog delves into the intricacies of building a 10-Q Analyzer—a tool I designed to process SEC 10-Q … Continue reading Building a 10-Q Analyzer: Part 1 | Extracting Financial Insights with AI
Understanding Production RAG Systems (Retrieval Augmented Generation)
1. What is RAG ? Retrieval Augmented Generation (RAG), is a method where you have a foundation model, and you have a library of personal documents – this can be unstructured data in any format. Now your goal is for answering some questions from your persona library of docs, with the help of LLM. Enter … Continue reading Understanding Production RAG Systems (Retrieval Augmented Generation)
Infinite Couch 