What is retrieval-augmented generation? More accurate and reliable LLMs

Retrieval-augmented generation (RAG) enhances large language models (LLMs) by incorporating external data sources to address issues like hallucination and limited context. The process involves retrieving relevant data, augmenting the model's prompt, and then generating a refined output. Although RAG shows promise, it is not an all-encompassing remedy since it may introduce new challenges and may become less relevant as LLMs advance. Recent architectures, such as those combining RAG with graph databases and agentic RAG, improve precision by connecting relationships to external knowledge sources.

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

Read at InfoWorld