prompt-engineering

To write better LLM prompts for reliable structured output, you need to define explicit roles, set clear constraints, and specify exact output formats to guide the model consistently and reduce hallucinations.

Few-shot prompt engineering is a technique where you provide concrete examples within your prompt to show the model exactly how to respond. Use it to ensure consistent formatting for tasks like classification and summarization.

You can enforce output parsing and prevent prompt injection by establishing strict guardrails, enforcing output schemas, and applying explicit constraints within your system prompts to control model behavior.

The best way to structure system prompts for code generation and analysis is to define clear roles, provide concrete examples, and state explicit output requirements to transform vague requests into reliable responses.

Yes, you can improve classification accuracy without external dependencies by applying iterative prompt refinement, adding few-shot examples, and setting explicit guardrails to constrain the model's responses.