Most current chatbots are limited by static training data, leading to obsolete information, struggles with contextual understanding, inaccuracies, and handling complex queries, necessitating advanced techniques like Retrieval-Augmented Generation (RAG) for real-time adaptability.
Current chatbots rely on NLP, machine learning, and frameworks like TensorFlow or PyTorch but face challenges such as limited contextual understanding, generic responses, inaccuracies without real-time data integration, and lack of adaptability to diverse queries.
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