prompt-engineer

Few-shot prompt templates provide examples within the prompt to guide LLM outputs. You can design and evaluate few-shot templates by structuring example inputs and outputs systematically, ensuring the model follows desired patterns across diverse domains.

Chain-of-thought prompt design structures prompts to guide an LLM through intermediate reasoning steps. Use chain-of-thought prompts when solving complex tasks requiring multi-step logic, ensuring reliable and verifiable outputs from the model.

Manage context and token budgeting by structuring prompts to prioritize essential information within the LLM's token limits. Effective context management ensures the model processes relevant constraints and system prompts without exceeding maximum token budgets.

The best way to test prompt designs is through systematic prompt testing and evaluation workflows. Iterate on prompt designs by running structured evaluations, analyzing output formatting, and refining few-shot examples or system prompts based on results.

Yes, this prompt design approach supports evaluation workflows across diverse domains. By applying structured prompt formats, context management, and systematic testing, you can reliably steer LLM behavior regardless of the specific domain or application.