Retrieval-augmented generation (RAG) systems improve answer accuracy by incorporating relevant external knowledge bases with large language models (LLMs). RAG pulls pertinent information from specified documents or databases before generating responses, allowing LLMs to access up-to-date and domain-specific knowledge rather than relying solely on pre-existing training data. This enhanced approach reduces the frequency of hallucinations—instances where the model generates plausible but incorrect information—by grounding responses in factual data[2][5].
For real-world implementation, organizations can integrate RAG with their existing workflows by using knowledge bases to augment LLM outputs, optimize retrieval strategies, and set thresholds for confidence in generated answers. Additionally, establishing human oversight processes for critical outputs further mitigates the risks associated with hallucinations in high-stakes applications[5]
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