Build an End-to-End RAG Pipeline for LLM Applications

**Build an End-to-End RAG Pipeline for LLM Applications**

Large language models have transformed the way we build intelligent applications. Generative AI Models can summarize documents, generate code, and answer complex questions. However, they still face a major limitation: they cannot access private or continuously changing knowledge unless that information is incorporated into their training data. Retrieval-Augmented Generation (RAG) addresses this limitation by combining information retrieval systems with generative AI models. Instead of relying entirely on the knowledge embedded in model weights, a RAG system retrieves relevant information from external sources and provides it to the language model during inference...

---

**[devsupporter 해설]**

이 기사는 DigitalOcean에서 제공하는 최신 개발 동향입니다. 관련 도구나 기술에 대해 더 알아보시려면 원본 링크를 참고하세요.