Category: large language model
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RAG Application with Cohere Command-R and Rerank – Part 1
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Introduction The Retrieval-Augmented Generation approach combines LLMs with a retrieval system to improve response quality. However, inaccurate retrieval can lead to sub-optimal responses. Cohere’s re-ranker model enhances this process by evaluating and ordering search results based on contextual relevance, improving accuracy and saving time for specific information seekers. This article provides a guide on implementing…
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LLMs Exposed: Are They Just Cheating on Math Tests?
Introduction Large Language Models (LLMs) are advanced natural language processing models that have achieved remarkable success in various benchmarks for mathematical reasoning. These models are designed to process and understand human language, enabling them to perform tasks such as question answering, language translation, and text generation. LLMs are typically trained on large datasets scraped from…
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RAG and Streamlit Chatbot: Chat with Documents Using LLM
Introduction This article aims to create an AI-powered RAG and Streamlit chatbot that can answer users questions based on custom documents. Users can upload documents, and the chatbot can answer questions by referring to those documents. The interface will be generated using Streamlit, and the chatbot will use open-source Large Language Model (LLM) models, making…