Knowledge Check

The following questions will help you check what you've learned.

1. Question 1 of 3You are working on developing an intelligent question-answering system for a company's internal knowledge base. The system must provide accurate and relevant answers to employees' queries by using the extensive information available in the knowledge base. Which business application of Retrieval Augmented Generation (RAG) would be the most suitable in this scenario?

   AGenerating high-quality content, such as articles, reports, or summariesBBuilding intelligent question-answering systems grounded in company-specific knowledgeCExpanding and enriching existing knowledge bases with new informationDTraining a large language model on the knowledge base
   Check answer
2. Question 2 of 3You are working on a project that requires customizing a large language model for a specific domain, such as legal or medical. The model must be highly accurate and tailored to the domain-specific terminology and knowledge. Which approach would provide the best trade-off between cost and performance for this scenario?

   AIn-context learningBRetrieval Augmented Generation (RAG)CPre-trainingDFine-tuning
   Check answer
3. Question 3 of 3You are developing a large-scale data processing pipeline that involves multiple steps, such as data ingestion, cleaning, transformation, and analysis. Each step requires different computational resources and dependencies. What would be the most important role of agents to ensure efficient and reliable completion of this multi-step task?

   AReporting and loggingBTask coordinationCScalability and concurrencyDIntegration and communications
   Check answer

You have completed the knowledge check for this section. Now let's look at the evaluating results stage of the generative AI application lifecycle.