News Business The Evolution of Chatbots: Key Technologies
Artificial intelligence

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The Evolution of Chatbots: Key Technologies

Chatbots have become ubiquitous in customer service, government departments, and various online services. However, their limitations stem from static training data, leading to outdated information and contextual challenges.

Challenges Faced by Current Chatbots

Existing chatbots rely on natural language processing (NLP), machine learning algorithms, and neural networks. Despite these technologies, they struggle with:

  • Contextual understanding, resulting in generic responses.
  • Inaccuracies and "hallucinations" due to lack of real-time data integration.
  • Inability to handle complex queries and adapt to evolving trends.

Retrieval-Augmented Generation (RAG)

RAG combines generative AI with real-time information retrieval from external sources. By augmenting their knowledge base dynamically, RAG chatbots offer contextually relevant responses and adaptability.

Technologies Empowering RAG

RAG systems utilize:

  • Frameworks and tools
  • Semantic analysis
  • Vector databases
  • Similarity search
  • Privacy/security applications

These components enhance chatbot capabilities, ensuring engaging and informative interactions.

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