prompt-engineer

Prompt design involves structuring instructions, examples, and constraints to guide LLM behavior. Use techniques like chain-of-thought reasoning, few-shot examples, and clear system prompts to maximize output quality and consistency across different tasks and model versions.

Zero-shot prompts ask the model to perform a task with no examples; few-shot prompts include examples in the input to guide behavior. Few-shot typically produces higher-quality outputs for complex tasks but uses more tokens, requiring trade-offs between quality and cost.

Define output schemas explicitly in your prompt or use JSON mode and function-calling APIs. Specify field names, types, and format requirements upfront, then test edge cases across model versions to ensure reliable, parseable structured outputs.

Build evaluation frameworks with metrics and test suites that measure prompt performance against expected outputs. Test across diverse tasks, edge cases, and model versions to validate quality, token efficiency, and latency before deployment.

Yes. Token-efficient prompt design reduces both latency and cost. Use concise instructions, remove unnecessary examples, and apply latency-aware techniques like streaming. Balance brevity with clarity to maintain output quality within your performance constraints.

Include constraints in system prompts to define model behavior boundaries, error handling, and edge-case responses. Test guardrails across model versions and diverse inputs to ensure consistent, safe behavior before deploying to production.