In-context learning (ICL) is a novel approach used in large language models (LLMs) that allows these models to perform new tasks using natural language prompts without the need for explicit retraining. Unlike traditional machine learning, which requires updating model parameters, ICL leverages pre-trained knowledge to generalize from a few input-output examples, a method often referred to as few-shot learning. This technique is enhanced by prompt engineering, which involves crafting effective prompts to guide the model's understanding and output. ICL's efficacy is closely linked to the model's scale, training data quality, and the specific domain it is applied to, showing competitive performance across various applications like sentiment analysis, language translation, and medical diagnostics. Despite its advantages, ICL faces challenges related to model size, data dependency, and domain specificity, and it presents ethical and security concerns. Research in this area is rapidly evolving, focusing on innovations like structured prompting and understanding the relationship between Transformer attention and gradient descent, which further enhance the capabilities of ICL in LLMs.