Understand large language models

A large language model, or LLM, ​is an AI model that is trained on large amounts ​of text to identify patterns between words, concepts, ​and phrases, so that it can generate responses to prompts. ​ An LLM is trained on millions of sources of text, ​including books, articles, websites, and more. ​This training helps the model learn the patterns ​and relationships that exist in human language. ​In general, the more high quality data the model receives, ​the better its performance will be. ​Because LLMs can identify so many patterns in language, ​they can also predict what word is most likely to come next ​in a sequence of words.

Although LLMs are powerful, ​you may not always get the output you want. ​Sometimes this is because of limitations ​in an LLM’s training data. ​For instance, an LLMs output may be biased ​because the data it was trained on contains bias. This data may include news articles ​and websites that reflect ​the unfair biases present in society.

 ​A number of factors can contribute to hallucinations, ​such as the quality of an LLM’s training data, ​the phrasing of the prompt, ​or the method an LLM uses to analyze text ​and predict the next word in a sequence