Get insights into the world of IT and its changing nuances.
Learn how Retrieval Augmented Generation (RAG) is changing customer support by improving chatbots and virtual assistants. This blog explains how Retrieval Augmented Generation (RAG) technology empowers chatbots with access to your organization's knowledge base while delivering accurate and personalized customer experiences.
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Agentic AI goes beyond just automating tasks. It allows systems to work autonomously and make smart decisions. This guide explains the core concepts, real-world use cases, and future impact of Agentic AI. Discover the full potential of Agentic AI in this blog.
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The impact of AI on the healthcare market is significantly increasing with advancements in machine learning, big data, and natural language processing. This blog examines the expected market size of AI in healthcare for 2025, the latest trends influencing its growth, and its application in diagnostics, drug discovery, and personalized treatments.
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Small Language Models (SLMs), which are effective, lightweight substitutes that provide improved security, reduced expenses, and quicker processing. In this article, we have covered how SLMs operate, key differences from LLMs, model compression strategies, real-world examples, advantages, difficulties, and new developments.
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Retrieval Augmentation Generation (RAG) is a critical framework in AI that provides accurate and up-to-date information. Explore what RAG is, its types of architectures, advanced techniques, use cases, and step-by-step process for development.
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RAG is advancing AI with real-time retrieval, hybrid search, and multimodal capabilities. Trends like personalized RAG, on-device AI, and scalable solutions will impact industries. This blog explores RAG’s future and its business potential in 2025 and beyond.
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RAG provides precise, context-aware results by fusing AI with real-time data retrieval, outperforming traditional search. It enhances knowledge retention, provides context-aware replies, and dynamically adjusts to changing facts. This blog explores its benefits, use cases, and future impact.
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Using your own data to train an LLM guarantees that it comprehends business requirements and industry-specific terminology. From model selection and data preparation to fine-tuning and deployment, this tutorial covers all the important phases. It also tackles issues like data privacy and computational expenses.
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Fraud detection and prevention have become a crucial topic in the FinTech industry. To tackle these challenges, AI and machine learning technologies have stepped in to identify unusual patterns and help businesses stay safe. Explore what fraud detection is and how AI helps prevent fraud quickly.
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AI is a crucial part of the FinTech industry, but it brings various challenges for businesses, making it difficult for companies to integrate this technology. We have listed the top 15 challenges in this blog.
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