NemoClaw Knowledge Wiki

rag-techniques

2 min read

🗂️ AI & Agents · View mindmap

Rag Techniques

Retrieval-Augmented Generation (RAG) is a method in artificial intelligence that enhances language model outputs by incorporating external knowledge sources. Rather than depending solely on information learned during model training, RAG systems retrieve relevant documents or data at inference time and integrate that information into the model’s response generation process. This approach allows AI systems to provide more current, accurate, and contextually appropriate answers than would be possible using only pre-trained knowledge.

How RAG Works

RAG systems operate through a two-stage process. First, a retrieval component searches a knowledge base or document collection to identify passages or documents relevant to a user’s query. Second, an augmentation component passes both the user’s query and the retrieved documents to a language model, which generates responses informed by this external context. This architecture enables the model to cite sources, handle domain-specific information, and incorporate recent data without retraining.

Applications and Benefits

RAG is particularly valuable in applications requiring up-to-date information, specialized knowledge, or factual accuracy, such as customer support systems, research assistants, and enterprise question-answering applications. By grounding language model outputs in retrieved documents, RAG reduces hallucinations—instances where models generate plausible-sounding but false information. It also allows organizations to leverage existing documentation and knowledge repositories without extensive model retraining.

Source Notes

Graph View

Backlinks