prompt-engineering

Craft prompts with clear task definitions, structured context, explicit constraints, and examples. Use techniques like chain-of-thought reasoning and few-shot learning to guide the model toward consistent, high-quality results across summarization, coding, classification, and other domains.

Chain-of-thought prompts ask models to show reasoning steps before answering, improving accuracy on complex tasks. Few-shot prompting provides input-output examples within the prompt to demonstrate the desired behavior and format without retraining.

Analyze prompt failures systematically, identify failure patterns, add targeted examples that address those cases, and retest. Version control your prompts and document which refinements improved consistency for specific tasks.

Yes. Define the output format explicitly in your prompt, provide examples of text-to-JSON conversion, specify constraints on field values, and use role prompts to set context. This reliably extracts product data, metadata, and other structured information.

A robust system prompt defines the assistant's role, sets behavioral constraints, specifies output formatting requirements, and includes fallback instructions for error cases. Combine role definition with explicit formatting and iterative testing against real queries.