LLM-Powered Data Extraction: From Unstructured to Structured

Research content comes in many forms: PDF reports, HTML pages, news articles, WeChat posts, meeting notes, and emails. Extracting structured data from these sources has traditionally required custom parsers for each format. LLMs offer a more flexible approach.

The Problem Space

Investment research teams receive hundreds of documents daily. Manually extracting key information like ticker symbols, price targets, supply/demand views, and sentiment is time-consuming and inconsistent. We needed an automated solution that could:

• Process multiple document formats uniformly
• Extract standardized fields reliably
• Handle domain-specific terminology
• Scale to high document volumes

Agent Design

The key insight was making the extraction process controllable. Rather than letting the LLM freely interpret documents, we designed structured prompts that specify exactly what fields to extract and how to format them.

Extracted Fields

• Entity identification: Ticker, company name, product
• Geographic context: Region, market
• Quantitative data: Price, volume, time series
• Qualitative views: Supply/demand outlook, sentiment

Implementation Challenges

Several challenges emerged during development:

• Hallucination control: LLMs sometimes generate plausible but incorrect data. We implemented validation against known reference data.
• Format consistency: Ensuring output tables follow exact schemas required careful prompt engineering and post-processing.
• Cost management: Processing large documents can be expensive. We implemented chunking strategies and caching.

Results

The system now processes research content into semi-structured tables that feed directly into our data lake. Analysts can query extracted insights without reading full documents, while maintaining links to source material for verification.

LLMs aren't replacing human analysis - they're accelerating the data preparation that enables deeper analysis.