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Data Curation Made Easy: Hybrid AI Approach

Across industries, businesses constantly struggle to extract valuable insights from complex documents such as financial reports or research papers. These documents often contain a mix of dense text, tables packed with data, and intricate charts. Manually pulling insights from such material is not only tedious but also slows down decision-making, introduces risk, and can limit a company’s ability to stay ahead in fast-paced markets. Now, imagine a solution that automates…
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Retrieval Augmented Generation (RAG) in simple business terms

1. What Is RAG? Imagine pairing a super‑fast search engine with a large language model (LLM). The search engine grabs the most relevant facts; the LLM turns those facts into clear answers. That combo is Retrieval‑Augmented Generation (RAG). 2. How Does It Work? Retrieve – The system quickly searches your chosen knowledge sources (public sites, private docs, etc.) and pulls out the fragments most related to the question. Augment –…
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Query languages—the Swiss army knife of information extraction

Text mining, the art of extracting information from text, requires the formulation of efficient queries that retrieve information based on user input. To do this, the user requires a language for writing queries. For the most basic use cases, the language operators could be regex or string search. But while regex and string search are indispensable for text mining, their utility hits a hard ceiling when semantic or meaningful search…
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What is Linguistic Corpus?

At the core of Natural Language Processing lies extensive language data. The availability of copious amounts of text or speech, faithfully representing natural language, is pivotal for the efficacy of artificial language systems. Diverse forms of text, from Wikipedia articles to Twitter threads, find application in various language models. These substantial language datasets collectively constitute what is known as a corpus in linguistics—an extensive and principled collection of authentic, naturally…
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