The phases of LLM engineering are as follows: pre-training, fine-tuning, and transfer-learning, alignment, and evaluating . Proper fine-tuning accompanied with prompt-engineering helps comprehend the insufficiency in data. Fine-tuning is used to tailor our LLM for a specific task of NLP , while prompt-engineering is to give the model certain examples to use as a guide in future predictions .