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WorksheetsRAG Quiz Answers
Total questions: 15
Worksheet time: 8mins
Which issue does RAG primarily help mitigate in large language models?
Syntax errors in tokenization
Hallucination and outdated knowledge
Model overfitting on small datasets
Slow training on GPUs
In a RAG pipeline, which component locates relevant documents for a query?
Generator component
Retriever component
Ranker component
Summarizer component
What kind of information is injected into the model to ground answers in RAG?
User demographic data
Factual domain information
Random web samples
Model hyperparameters
In RAG, what are the model inputs typically enhanced with before generation?
Retrieved text snippets
Optimizer gradients
Training labels
System prompts only
What does the acronym RAG stand for?
Retrieve and Guess
Retrieval and Generation
Reasoning and Guidance
Ranking and Grouping
Embeddings used by retrievers are stored as what mathematical objects?
Matrices of logits
Vectors of numbers
Trees of nodes
Graphs of edges
Which library is commonly used for efficient similarity search over embeddings?
NumPy
FAISS
TensorFlow
Pandas
One benefit of RAG over purely parametric models is that external knowledge can be changed how?
Data can be updated outside the model
Weights are rewritten during inference
Prompts auto-tune themselves
Embeddings never require refresh
In RAG, the generator’s role is best described as what?
Selecting top-k documents
Producing final response using context
Computing embedding vectors
Indexing documents for retrieval
A typical retriever operation returns what from the index?
All documents ever seen
Top-1 nearest neighbor
K most relevant documents
Only exact keyword matches
Which limitation of base LLMs motivates augmenting them with retrieval?
No access to real-time data
Inability to do arithmetic
Poor sentiment detection
Lack of tokenization rules
RAG is especially helpful for which type of workload?
Creative fiction writing tasks
Domain-specific factual tasks
General chit-chat conversations
Adversarial prompt defense
Effective retrieval depends most on which capability?
Query understanding
Grammar correction
Topic generation
Model pruning
Similarity search in vector databases is based on what notion?
Lexical overlap
Semantic similarity
Syntactic distance
Statistical frequency
Overall, adding retrieval to generation has what typical effect on answers?
Increases narrative creativity
Enhances factual accuracy
Reduces token counts significantly
Eliminates need for prompts
