Case Study: Development of a RAG Intelligent Search Engine
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▶️ Discover how intelligent automation revolutionized the management of court notifications in our case study.
▶️ Learn how we implemented a RAG search engine to optimize complex processes, explained with concrete results.
▶️ Explore a practical example of how AI transforms the automation of legal tasks and information search.
What is a RAG Search Engine?
A RAG (Retrieval Augmented Generation) search engine is an advanced type of search engine that combines two key technologies:
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Information Retrieval: The system searches and extracts relevant data from large knowledge bases or documents, as a traditional search engine would do.
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Natural language generation: A generative AI, such as advanced language models, takes the retrieved information and presents it in a well-structured, coherent and personalized response, rather than simply listing results.
This type of search engine excels at processing and understanding both structured and unstructured data (such as documents, records, etc.), using semantic capabilities to perform more precise contextual searches. Rather than simply displaying textual results like traditional search engines, RAG search engines generate answers based on the exact context of the query, combining the best of search and content generation.
These systems are revolutionizing the way information is searched, especially in complex areas such as the processing of large volumes of documents, for example, in hospitals, law firms or corporations that need access to detailed information in an understandable format adapted to each query.