LangChain has been working on improving the performance of large language models (LLMs) in tool-calling applications, particularly through the use of few-shot prompting, which involves providing example model inputs and outputs to enhance model performance. Experiments were conducted using two datasets, Query Analysis and Multiverse Math, to evaluate different few-shot techniques by benchmarking against OpenAI and Anthropic models. The results indicated that few-shot prompting significantly boosts model performance, especially when semantically similar examples are used, and the effectiveness of the technique varies depending on the model and task. The study highlighted that the format and selection of few-shot examples are crucial for performance, with smaller models like Claude 3 Haiku showing marked improvement and rivaling the zero-shot performance of larger models. It also emphasized the importance of evaluating the configuration of few-shot systems based on specific models and tasks, opening avenues for future exploration in optimizing few-shot prompting strategies and their application in LLMs.