Contextual AI Enterprise RAG Quiz

Answers and explanations appear as you take the quiz.

1. What does the acronym RAG stand for?
   • Retrieval-Augmented Generation
   • Recursive Algorithm Generation
   • Ranked Answer Grounding
   • Real-time API Gateway
2. What connection does Contextual AI co-founder Douwe Kiela have to the original 2020 RAG paper?
   • He was the paper’s only author
   • He is one of the paper’s co-authors
   • He founded the research lab that published the paper
   • He led the company that acquired the paper’s dataset
3. What is the stated training goal of Contextual AI’s grounded language model?
   • Guarantee that every answer is factually correct
   • Replace document retrieval with the model’s internal memory
   • Base answers on retrieved passages and express uncertainty when evidence is missing
   • Satisfy every regulatory requirement by adding a citation
4. What does a reranking step do in a RAG pipeline?
   • Deletes duplicate documents permanently
   • Encrypts the source data
   • Trains the base model from scratch
   • Reorders retrieved candidates by relevance before generation
5. What advantage does Contextual AI claim over a generic chatbot bolted onto a vector database?
   • Every component is jointly tuned as one optimized system
   • It needs no documents at all
   • It runs entirely without computers
   • It only works on public web data
6. How can citations help a reviewer assess an enterprise RAG answer?
   • They automatically satisfy regulatory compliance
   • They let the reviewer trace claims to retrieved source passages
   • They prove every model output is correct
   • They eliminate the need to check data access and retention
7. What document feature does Contextual AI's parser aim to preserve for enterprise RAG?
   • Only the file's creation timestamp
   • The model's private training weights
   • Document structure such as hierarchy and tables
   • A chat history unrelated to the source document
8. Which evaluation component does Contextual AI list alongside parsing, reranking, and generation?
   • A GPU scheduler for model training
   • An image upsampler for document figures
   • A biometric login service for analysts
   • LMUnit for preference, direct-scoring, and natural-language unit-test evaluation