prompt-engineering

Chain-of-thought prompting improves LLM accuracy by guiding the model through step-by-step reasoning before generating a final answer. This Skill offers guidance on implementing chain-of-thought patterns to achieve reliable outputs for complex tasks.

Zero-shot learning provides instructions without examples, while few-shot learning includes sample inputs and outputs to guide the LLM. This Skill implements both methods, helping you choose the right pattern to reduce costs and improve result consistency.

You can manage prompt versioning and cost monitoring for multi-model deployment using production best practices outlined in this Skill. It provides systematic techniques for tracking prompt iterations and controlling expenses across different large language models.

Yes, you can use these prompt engineering patterns with Python and TypeScript. This Skill provides examples for both languages, ensuring you can implement zero-shot, few-shot, and structured output methods effectively in your development environment.

Your LLM output is inconsistent because it lacks structured prompt engineering techniques like few-shot learning and chain-of-thought reasoning. This Skill addresses the challenge of unreliable outputs by providing patterns to improve accuracy and reduce costs.