WorksheetsRAG -3
Total questions: 1
Worksheet time: 7mins
1-10.
Answer the questions below after watching the video
What is the first step in deploying Flow-wise to Render?
Injecting context into prompts
Splitting data into chunks
Creating a new chatbot
Connecting to a data source
What does RAG stand for?
Retrieval Application Generation
Render Application Guide
Rapid Application Generation
Retrieval Augmented Generation
Which model is used in the retrieval chatbot setup?
BERT
Transformer
ELMo
GPT-3
What is the purpose of splitting data into smaller chunks?
To increase the token usage
To make the chatbot more creative
To reduce the context limit exceeded error
To increase the speed of data retrieval
What is the primary function of the vector store in the chatbot setup?
To increase the chatbot's response time
To create a backup of the chatbot's memory
To store numeric representations of data
To store user credentials
Why is it important to convert data into a numeric representation?
To increase data storage capacity
To make the data more secure
To enable the vector store to retrieve relevant documents
To make it readable by humans
What tool is used to scrape information from a website?
Notion database connector
Cheerio web scraper
PDF file uploader
AWS S3 bucket fetcher
How can you ensure the chatbot does not 'hallucinate' answers?
By adding more memory
By using a different chat model
By using a higher temperature setting
By providing context from the data source
What is the purpose of the 'upsert' process in the chatbot setup?
To update the chatbot's memory
To fetch and load data into the vector store
To change the chatbot's temperature setting
To connect the chatbot to different data sources
What is the main advantage of using a Pinecone serverless database?
It allows for creative responses
It provides persistent data storage
It increases the speed of the chatbot
It reduces the need for data chunking
