Prompt engineering emerged as a genuine skill in 2023. The gap between a developer who understands how to effectively instruct LLMs and one who does not is visible in application quality.
Be specific about format and length
LLMs will produce variable-format outputs unless you specify exactly what you want. Specify: the format (JSON, markdown, plain text, numbered list), the length (one paragraph, 500 words, three bullet points), and the structure (include these fields, in this order). Ambiguity in format requirements produces inconsistent outputs that are hard to parse programmatically.
Few-shot examples outperform description alone
Showing the model examples of the input-output behaviour you want is more reliable than describing it. If you want the model to classify support tickets into five categories, providing three examples of each category alongside the classification beats a text description of the categories. The model learns the boundary conditions from examples better than from abstract descriptions.
Role prompting and persona
Telling the model it's a specific type of expert (a senior software engineer reviewing code, a financial analyst reviewing a report) activates the model's knowledge about how that type of expert reasons and communicates. This is not magic but it does produce more domain-appropriate outputs than generic prompts. 'Review this code for security issues' produces different outputs than 'You are a security engineer specialising in web applications. Review this code'.
Chain of thought for complex tasks
For tasks that require multi-step reasoning, asking the model to 'think step by step' before giving a final answer produces higher accuracy. The intermediate reasoning steps keep the model on track and make errors more detectable. This is especially important for mathematical and logical tasks where the final answer without reasoning is hard to verify.