The video provides a detailed walkthrough of creating high-accuracy workflows for identifying company mentions in financial documents using LLMs and web scraping tools.
Key challenges include handling hallucinations from LLMs, overcoming content limitations due to context window limits, and bypassing paywalls during data scraping.
A combination of two different LLMs, Gemini and Anthropic's Claude, is utilized, each trained with a meticulous prompt to identify company names accurately. A formula then consolidates the results to filter only the companies from the original list.
Step-by-Step Guide
Define the Use Case:
Start with a list of companies and the task of identifying mentions within specific financial documents, such as earnings call transcripts.
Understand the Challenges:
Hallucinations: LLMs may invent company names not actually present in the documents.
Context Window Limits: Large document sizes can exceed token limits in tools like Clay.
Data Scraping:
Use tools like ZenRose or Clay's integrated scraper to scrape the earnings call transcripts, utilizing specific settings to bypass paywalls.
Settings: Use the premium proxy and ask for body text output without auto-parse to enhance accuracy and bypass paywall restrictions.
LLM Selection and Prompting:
Choose LLMs: Use multiple LLMs (e.g., Gemini and Anthropic's Claude) to ensure diverse perspectives and reduce hallucinations.
Develop Prompting Framework: Use a structured prompt consisting of role, task, rules, and output instructions.
Role: Assign the LLM as a financial analyst with expertise in identifying company names.
Task: Outline steps for reading documents and identifying company names.
Rules: Instruct the LLM to focus only on the provided data, avoiding external knowledge.
Output: Specify formatting requirements for the output, such as sorting company names alphabetically.
Data Processing:
Run the same prompt on both LLMs, leveraging different strengths for optimal results.
Use formulas to combine results from both LLMs and filter out duplicates, focusing on unique company names.
Verification:
Implement a filtering formula to cross-reference extracted company names with the original list.
Manually verify results to ensure no hallucinations and confirm accuracy.
Optimization and Fuzzy Matching:
Consider implementing fuzzy matching to catch slight variations in company names.
Balance the trade-off between achieving maximum accuracy and avoiding false positives.
Evaluate and Improve:
Continuously refine the workflow based on outcomes and explore potential optimizations.
Keywords
LLMs, web scraping, high-accuracy workflows, financial document analysis, earnings call transcripts, company mention detection, hallucinations in AI, data scraping, prompt engineering, Gemini, Anthropic Claude, ZenRose scraper, context window limits, financial analyst AI, prompt framework, fuzzy matching, accuracy optimization.
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