Large Language Models (LLMs) are designed to overcome limitations in general-purpose use cases by integrating specific data sets and contexts.
RAG pipelines provide scalable solutions that enable applications such as content creation, QA, and code generation by mitigating issues like outdated knowledge and resource requirements.
Challenges addressed through RAG include hallucinations, prompt injection, and high resource requirements, which are mitigated by providing context to the models and augmenting user queries.
The goal of RAG is to create a seamless experience for users, allowing them to retrieve relevant information quickly and accurately while reducing errors or misinterpretations through smooth integration of LLM Solutions.
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