Fine-Tuning
The process of further training a pre-trained AI model on domain-specific data to improve its performance for a particular task. In finance, fine-tuning on financial documents, earnings calls, and SEC filings improves the model's understanding of industry terminology and analytical frameworks.
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NLP (Natural Language Processing)
A branch of artificial intelligence that enables computers to understand, interpret, and generate human language. In finance, NLP is used to extract insights from earnings calls, filings, and news articles — powering everything from sentiment analysis to automated report generation.
RAG (Retrieval-Augmented Generation)
An AI architecture that combines information retrieval with text generation, allowing the model to ground its responses in specific source documents. Reduces hallucinations by anchoring outputs to cited data — a critical requirement for financial research where accuracy is non-negotiable.
Sentiment Analysis
The use of NLP to determine the emotional tone behind text — positive, negative, or neutral. Applied in finance to gauge management confidence in earnings calls, market sentiment in news coverage, and investor mood across social media and analyst reports.
Document Intelligence
AI-powered extraction and analysis of structured and unstructured data from documents such as financial filings, reports, and presentations. Goes beyond simple OCR to understand context, tables, and relationships between data points across thousands of pages.
Large Language Model (LLM)
A deep learning model trained on vast amounts of text data, capable of understanding and generating human-like text. The core technology behind AI research platforms like DataToBrief. Modern LLMs can analyze financial documents, synthesize information across sources, and generate institutional-grade research.
Named Entity Recognition (NER)
An NLP technique that identifies and classifies named entities in text — such as company names, ticker symbols, people, and monetary values. Essential for extracting structured data from unstructured financial documents and building knowledge graphs for investment research.
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