MTech in Machine learning, DA-IICT | Passion for NLP, Recommendation System | Creating conversational agents using LLM.
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Retrieval-augmented generation (RAG) has transformed the way large language models (LLMs) generate responses by integrating external data. Hybrid RAG, an advanced approach, combines vector similarity search with traditional methods like BM25 and keyword search, enabling more robust and flexible information retrieval. Hybrid RAG is an advanced retrieval technique that merges
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Retrieval-augmented generation (RAG) improves large language models (LLMs) by integrating external data, enhancing the relevance and accuracy of outputs. Instead of relying solely on pre-trained knowledge, RAG fetches and uses information from external sources like vector databases, making it ideal for domain-specific or up-to-date tasks. However, traditional RAG has limitations,
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Large Language Models (LLMs) have transformed artificial intelligence by understanding and generating human-like text. For AI development teams—including engineers, researchers, data scientists, and product developers—accurately assessing these models is vital. Benchmarking evaluations offer insights into the strengths and weaknesses of different LLMs, guiding further research and development. Drawing
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As AI development teams increasingly leverage pre-trained language models (PLMs) for diverse applications, understanding the nuances of model adaptation techniques becomes crucial. This article provides an in-depth technical comparison of fine-tuning and prompt tuning, specifically tailored for AI engineers, researchers, data scientists, and product developers seeking to optimize PLM performance
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Retrieval Augmented Generation (RAG) represents a significant advancement in the field of natural language processing (NLP), merging the strengths of retrieval-based systems and generative models. This hybrid approach not only enhances the accuracy and relevance of generated responses but also ensures that the information remains current without the need for
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Retrieval Augmented Generation (RAG) systems are the backbone of an AI pipeline as they augment LLMs with external knowledge. This ensures that AI responses are both precise and contextually relevant by grounding them in factual data. This guide provides actionable insights for AI development teams, helping you build robust, scalable,