Browse all content tagged with Information Retrieval
Agentic RAG (Agentic Retrieval-Augmented Generation) is an advanced AI framework that integrates intelligent agents into traditional RAG systems, enabling autonomous query analysis, strategic decision-making, and adaptive information retrieval for improved accuracy and efficiency.
Content Enrichment with AI enhances raw, unstructured content by applying artificial intelligence techniques to extract meaningful information, structure, and insights—making content more accessible, searchable, and valuable for applications like data analysis, information retrieval, and decision-making.
Document grading in Retrieval-Augmented Generation (RAG) is the process of evaluating and ranking documents based on their relevance and quality in response to a query, ensuring that only the most pertinent and high-quality documents are used to generate accurate, context-aware responses.
Extractive AI is a specialized branch of artificial intelligence focused on identifying and retrieving specific information from existing data sources. Unlike generative AI, extractive AI locates exact pieces of data within structured or unstructured datasets using advanced NLP techniques, ensuring accuracy and reliability in data extraction and information retrieval.
Information Retrieval leverages AI, NLP, and machine learning to efficiently and accurately retrieve data that meets user requirements. Foundational for web search engines, digital libraries, and enterprise solutions, IR addresses challenges like ambiguity, algorithm bias, and scalability, with future trends focused on generative AI and deep learning.
Perplexity AI is an advanced AI-powered search engine and conversational tool that leverages NLP and machine learning to deliver precise, contextual answers with citations. Ideal for research, learning, and professional use, it integrates multiple large language models and sources for accurate, real-time information retrieval.
Query Expansion is the process of enhancing a user’s original query by adding terms or context, improving document retrieval for more accurate and contextually relevant responses, especially in RAG (Retrieval-Augmented Generation) systems.
Retrieval Augmented Generation (RAG) is an advanced AI framework that combines traditional information retrieval systems with generative large language models (LLMs), enabling AI to generate text that is more accurate, current, and contextually relevant by integrating external knowledge.
