Generative AI models respond to natural language questions. It interprets the questions of the users and the instructions too. The same are converted into machine language to produce the output. In GPT 3, the questions are converted into SQL or structured query language. The same is run on a database to fetch the required answer. It is necessary to learn the way the prompts are written. A prompt could be a general statement — compose a poem on Paris. A prompt engineer frames the question asking the model to write about the museums of Paris by making it specific or asking it to write about Paris as the fashion capital. Such prompts will produce the best output. The queries could be framed differently for different language models. GPT 3 and ChatGPT are preferred because these models keep client’s sensitive data concealed while generating a code. It is called tokenisation.