RAG -3

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