prompt-engineering

Few-shot examples improve LLM reasoning by providing input-output pairs within the prompt that demonstrate the desired pattern. This technique guides the model to recognize task structures and generate more accurate, consistent responses.

Chain-of-thought prompting instructs an LLM to break down its reasoning process step-by-step before providing a final answer. It is needed for complex tasks like code generation or decision support where logical accuracy is critical.

Create a system prompt by assigning a specific role to the model and defining strict constraints on its responses. This structured prompting technique guides the model's behavior, ensuring outputs align with your task requirements and constraints.

LLM output formats break during code generation when prompts lack explicit structure and evaluation criteria. Applying output parsing templates and few-shot examples standardizes the response format, reducing retries and formatting errors.

Yes, you can apply prompt engineering for decision support and content creation by tailoring prompts with role assignments and constraints. This approach guides the model to generate reliable, structured outputs across diverse domains.