Prompt Engineering for Reliable LLM Outputs

The same model can be flaky or dependable depending on how you ask. Reliable output comes from structure, not from clever wording.

• Be explicit about the output format—and validate the result.
• Give examples of good answers (few-shot).
• Break complex tasks into smaller steps.
• Ground the model in real context to reduce hallucination.

We keep a set of example inputs and expected behaviours, so a prompt change is evaluated, not just eyeballed once and shipped.