Speculative RAG Implementation With Transformers

Large Language Models (LLMs) are often out of their mind in knowledge-intensive tasks that require the latest and accurate information. This is where Retrieval Enhanced Generation (RAG) technology comes in, which combines the generational capabilities of LLM with an external knowledge base for improved accuracy and relevance.

However, traditional RAG systems face challenges when dealing with lengthy and complex documents, resulting in increased latency and sometimes decreased accuracy of results. To solve these problems, the concept of speculative RAG came into being and became a promising solution. Let's learn more about it.

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