Retrieval-Augmented Generation (RAG) Quiz

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Which of the following best describes the architecture of RAG? A language model augmented by token embeddings A combination of a language model and a retrieval component A transformer-based model only A reinforcement learning algorithm

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In RAG, what role does the "retrieval" component play? It generates tokens for the language model It retrieves relevant data from external knowledge sources It tunes hyperparameters of the model It improves the speed of response generation

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What type of external knowledge sources are typically used in RAG systems? Pre-trained embeddings Large databases, documents, or knowledge bases Image data Randomly generated data

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RAG differs from traditional LLMs by: Using fewer parameters Utilizing external data to improve generation accuracy Working exclusively with structured data Requiring no training at all

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Which of the following is a benefit of using RAG over pure language generation models? Faster generation time Improved context relevance by using real-time information Eliminates the need for tokenization Reduces model size significantly

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What type of retrieval methods are typically used in RAG systems? Neural networks only Vector-based retrieval (e.g., FAISS, Milvus) Manual indexing Image-based retrieval

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How does RAG improve responses in real-world applications? By generating all answers using internal knowledge By retrieving the most relevant external data to enhance response accuracy By reducing response time through simplification By eliminating the need for training on large datasets

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Which of the following frameworks is often used to build RAG pipelines for large-scale applications? FastText TensorFlow Hub PyTorch Lightning LangChain

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What is a common challenge faced by RAG models? Overfitting to a small dataset Ensuring relevance and freshness of retrieved data Inability to generate coherent text Slower model training time

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In a RAG system, how does vector-based retrieval function? It retrieves documents by computing the similarity between vectors of queries and documents It retrieves documents based on keyword matching It retrieves images based on pixel comparison It retrieves data based on the time of entry

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What type of data is typically stored in the retrieval component of a RAG system? Only structured data Unstructured text, documents, or embeddings Image data Audio data

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Which of the following is NOT a use case for RAG? Legal research Generating investment insights from financial reports Image recognition Personalized healthcare recommendations

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How does RAG handle the issue of hallucination common in large language models? By limiting token generation By pre-training on larger datasets By using reinforcement learning By incorporating external knowledge retrieval, thus reducing reliance on incomplete internal data

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By incorporating external knowledge retrieval, thus reducing reliance on incomplete internal data Dense vector retrieval using embeddings TF-IDF n-gram indexing BM25

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In a large-scale RAG system, what techniques are employed to handle latency issues when performing real-time retrieval? Sharding the retrieval database and using distributed retrieval architectures like FAISS or Milvus. Reducing the complexity of the language model while maintaining a large knowledge base. Implementing slower retrieval techniques to ensure higher accuracy. Using rule-based systems to speed up the retrieval process.

Retrieval-Augmented Generation (RAG) Quiz

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Retrieval-Augmented Generation (RAG) Quiz

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