Category: AI Chatbot
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Memory- The Soul of Intelligence in a AI RAG Chatbot
Introduction In any AI RAG (Retrieval‑Augmented Generation) chatbot, memory is the true soul of intelligence. Without memory, the system is just a reactive engine – answering queries in isolation. With memory, however, the chatbot becomes a dynamic, evolving partner that learns, adapts, and reasons more like a real human team. When you use tools like…
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Multi Agent AI RAG Chatbot
Introduction An AI RAG (Retrieval‑Augmented Generation) chatbot becomes more powerful when built as a multi‑agent system. Instead of one generalist model, it orchestrates specialized agents — such as a Sales Consultant for product recommendations, a Customer Service Officer for CRM queries and support, and a Sales Architect for bundles and strategy. What is Multi-Agent AI…
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Retrieval Sources in AI RAG Chatbot
Introduction The “R” in “RAG” stands for the Retrieval process, which is the step where the system fetches relevant information from external sources such as relational databases or PDF file to provide context for the AI model. This makes it clear that Retrieval is the first stage in the pipeline, and those sources (query, database,…