DeRel @ Athina AI
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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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Retrieval-augmented generation (RAG) improves the large language model's (LLM) response by using external data to enhance the relevance and accuracy of their outputs. Instead of relying only on pre-trained knowledge, RAG allows models to fetch and integrate information from external sources, such as vector databases. This approach is
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The new Claude Sonnet 3.5 model demonstrates significant improvements, particularly in mathematical reasoning, coding, visual understanding, and agentic tool use. These enhancements make it better at handling complex problems and more versatile across various tasks. Users can expect improved accuracy with this version. In this tutorial, we will guide
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Retrieval-Augmented Generation (RAG) is becoming popular for building applications with large language models (LLMs). RAG improves generative models by using external information to make their responses more accurate and context-aware. This guide explains how basic RAG works and how to evaluate the RAG pipeline with specific metrics, using simple steps