The text discusses experiments conducted on two datasets to improve LLM tool-calling performance using few-shot prompting. Few-shot prompting involves providing example model inputs and desired outputs in the model prompt, which can significantly boost model performance across a range of tasks. Experiments were run on Query Analysis and Multiverse Math datasets with different OpenAI and Anthropic models. Results showed that few-shotting with semantically similar examples as messages generally performed better than using static examples or all examples combined. The Claude models improved more with few-shotting compared to GPT models, and smaller models with few-shot examples could rival the zero-shot performance of larger models. Future work includes exploring negative few-shot examples, methods for semantic search retrieval, optimal number of few-shot examples, and comparing trajectories in agentic workloads.