This article explores four key methods—prompting LLMs, building retrieval-augmented generation (RAG) systems, fine-tuning LLMs and developing AI agents—and evaluates their role in shaping the ...

Retrieval-augmented generation, or RAG, integrates external data sources to reduce hallucinations and improve the response accuracy of large language models. Retrieval-augmented generation (RAG ...

TrustRAG is a configurable and modular Retrieval-Augmented Generation (RAG) framework designed to provide reliable input and trusted output, ensuring users can obtain high-quality and trustworthy ...

RAGFlow is an open-source RAG (Retrieval-Augmented Generation) engine based on deep document understanding. It offers a streamlined RAG workflow for businesses of any scale, combining LLM (Large ...