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?
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.