Prompting 101 | Code w/ Claude

Prompt engineering functions as an iterative, empirical science requiring clear task definitions, structured context, and precise instructions to optimize model performance. Effective prompts utilize XML tags to organize information, such as static form layouts or specific user preferences, which helps models like Claude process complex data more accurately. Incorporating few-shot examples and defining strict output formats—such as requiring final verdicts in dedicated tags—significantly reduces hallucinations and improves reliability in specialized tasks like insurance claim analysis. Furthermore, guiding the model through a logical, step-by-step reasoning process ensures that factual claims are grounded in provided evidence. Advanced techniques, including prompt caching and prefilling responses, further refine output consistency, while leveraging extended thinking capabilities allows for deeper analysis of complex inputs without sacrificing efficiency.