RAG vs. Fine Tuning: Which One is Right for You?
LLM, an acronym for Large Language Model, is an AI model developed to understand and generate human-like language. LLMs are trained on huge data sets (hence “large”) to process and generate meaningful and relevant responses based on the input they receive from an interaction. These data sets come from various sources, from websites to books, articles, and other text-related resources.
But while LLMs are incredibly powerful, they also come with limitations. One of the most common limitations of LLMs is hallucinations. Hallucinations occur when an AI model fabricates a confident but inaccurate response. This issue can be caused by many factors, including divergences in the source content when the data set is incredibly vast or flaws with how the model is trained. The latter can even cause a model to reinforce an inaccurate conclusion with its previous responses. This often raises concerns about its potential to spread misinformation or disinformation if not used responsibly.
But there is always a solution for every problem. In this article, we will cover the two most common ways to reduce hallucinations in LLMs.
