RAG

Large language models (LLMs) are not perfect. Researchers are exploring alternative ways to steer these models away from the potential challenges that lie in the path. At times, these models are unbelievably meticulous and spot on in nailing responses. But other times, they spit out answers that are fabricated and inaccurate.

Managing this volatility has been one of engineers’ biggest challenges so far. How can you build a model that is always right? More importantly, how can you get the model to say “I don’t’ know” when it doesn’t know an answer.

Data engineers have come up with a framework that takes a shot at solving this problem. Retrieval-augmented generation, also called RAG, is an AI framework that enables models to fact-check a response before giving it back.

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