Five Prompt Engineering Techniques That Actually Improve LLM Output

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A software engineer has outlined evidence-based prompt engineering practices that reliably improve large language model outputs, separating them from common superstitions. Key techniques include specifying exact output formats, providing few-shot examples, and prompting the model to reason step-by-step before answering. Assigning a contextually relevant role and breaking complex tasks into smaller chained prompts also measurably enhance results. The author warns against vague superlatives, excessive caveats, and politeness tokens like 'please', calling them wasted context. The core advice is to treat a prompt as a precise specification rather than a wish, adding clarity instead of magic phrases when outputs underperform.

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