Unverified50% confidenceFactExact time
The core idea of RAG is to retrieve the most relevant text fragments from an external knowledge base as context to feed into the model before passing the user's question to the LLM, in order to answer questions beyond the training data and reduce hallucinations.
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50%
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Long-term
Relevance
9/11/2026
First Seen
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Related Claims
UnverifiedDify provides a RAG knowledge base (Retrieval-Augmented Generation) feature that connects private documents to applications, allowing models to answer questions based on user data.75% similarUnverifiedRAG (Retrieval-Augmented Generation) uses a personal knowledge base as a private corpus, so that when AI answers questions it preferentially retrieves the user's own note content.73% similarUnverifiedRAG能实时索引API文档、Stack Overflow答案和依赖库变更日志，缓解模型的幻觉问题并提升生成代码与项目结构的一致性70% similarUnverifiedLLM-based information extraction solutions can quickly adapt to different business scenarios through prompt engineering, requiring only modifications to field definitions and examples in the prompt, without retraining the model.69% similarUnverifiedTaking LLMs as an example, context includes the system prompt, user query, externally retrieved information, historical interaction records, and additional system outputs.69% similar
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