prompt-engineering

To enforce structured JSON output, apply structured output patterns with defined schemas and validation tips, combining them with role-play and few-shot examples to constrain the model's response format reliably.

The best way to reduce AI hallucinations in data extraction pipelines is to formalize prompt behavior using techniques like few-shot examples, chain-of-thought, and self-consistency for repeatable and reliable responses.

Use few-shot prompting to demonstrate expected code formats or patterns, and apply chain-of-thought when the code generation task requires complex multi-step reasoning before producing the final output.

Yes, you can implement role-play and ReAct techniques without external dependencies by structuring your prompts to define specific roles and reasoning-action loops directly within the model instructions.

Design prompts for safety-aware decision support by specifying safety considerations directly within the prompt structure and using self-consistency or tree-of-thought techniques to validate reasoning paths before outputting decisions.