Knowledge base answer generation best practices

Answer generation combines the capabilities of a language model with knowledge bases. Answer generation retrieves relevant data from knowledge base articles and then uses that data to generate contextual, accurate, and dynamic responses. This article describes when and how to use answer generation in your digital bot.

Answer generation is a powerful tool that enhances digital bot responses. This method is most beneficial for the following types of content:

  • Content that changes dynamically
  • Large amounts of data 

Note: Answer generation is less beneficial in these areas: 
  •  Static or general knowledge: For more well-known, simple, or unchanging facts; for example, “What is the capital of France?”, answer generation is unnecessary. 
  •  Speed-critical and time-sensitive cases:  Answer generation involves an additional retrieval step, which can slow down response times.

These scenarios describe situations where answer generation is the optimal choice for bot response generation, along with best practices to help you implement answer generation effectively and maximize its potential. By following these best practices, you can promote a smooth and impactful user experience. 

Content that changes dynamically 

Answer generation can help with dynamically changing content that occurs when you do not have time to structure the real-time content into blocks. 

  •  Examples: News, technology updates, events, or any real-time information. 
  •  Use case: A customer asks, “What are the latest tech trends in AI?” or “What’s the weather forecast for today?” 

Large amounts of data 

 Answer generation is helpful when users require answers from vast, unstructured datasets. Answer generation enables the bot to retrieve the most relevant information quickly and also present it in a digestible format. 

  •  Examples: Customer service archives, technical documentation, and knowledge bases. 
  •  Use case: A customer asks, “How do I troubleshoot this error?” or “Can you list the name of the winner teams of basketball since 1960 in alphabetical order?” 

Answers that use multiple sources

 Because answer generation successfully aggregates insights from multiple documents to generate comprehensive, coherent answers, the process helps administrators with information gathering and time management.  

  •  Examples: Complex queries that require synthesizing information from different sources. 
  •  Use case: A customer asks, “What are the key differences between these three products?” or “What’s the best route for my trip combining train and flight options?”