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Retrieval-Augmented Generation (RAG) Quiz

Retrieval-Augmented Generation (RAG) is a powerful technique that enhances the accuracy and relevance of responses generated by AI by integrating external data sources. This skill is vital for applications requiring up-to-date and contextually rich information, such as chatbots, search engines, and knowledge retrieval systems.

In this quick 15-minute quiz, you’ll assess your understanding of RAG, including its architecture, components like retrieval systems and large language models (LLMs), and practical applications. Test your knowledge to ensure you’re equipped with the insights needed to leverage RAG in building more intelligent, responsive AI systems.

Type: MCQ
Time: Unlimited
No of Questions: 15

Page 1 of 15

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

Page 2 of 15

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

Page 3 of 15

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

Page 4 of 15

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

Page 5 of 15

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

Page 6 of 15

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

Page 7 of 15

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

Page 8 of 15

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

Page 9 of 15

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

Page 10 of 15

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

Page 11 of 15

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

Page 12 of 15

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

Page 13 of 15

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

Page 14 of 15

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

Page 15 of 15

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.

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