LLMs have a tendency to generate responses that sounds coherent and convincing but can sometimes be made up. Improving prompts can help improve the model to generate more accurate/factual responses and reduce the likelihood to generate inconsistent and made up responses.

Some solutions might include:

  • provide ground truth (e.g., related article paragraph or Wikipedia entry) as part of context to reduce the likelihood of the model producing made up text.
  • configure the model to produce less diverse responses by decreasing the probability parameters and instructing it to admit (e.g., "I don't know") when it doesn't know the answer.
  • provide in the prompt a combination of examples of questions and responses that it might know about and not know about

Let's look at a simple example:


Q: What is an atom? 
A: An atom is a tiny particle that makes up everything. 

Q: Who is Alvan Muntz? 
A: ? 

Q: What is Kozar-09? 
A: ? 

Q: How many moons does Mars have? 
A: Two, Phobos and Deimos. 

Q: Who is Neto Beto Roberto? 


A: ?

I made up the name "Neto Beto Roberto" so the model is correct in this instance. Try to change the question a bit and see if you can get it to work. There are different ways you can improve this further based on all that you have learned so far.