Editor's Synopsis
Microsoft's Azure Foundry documentation provides a comprehensive guide to prompt engineering techniques for GPT models, covering foundational concepts through advanced strategies. The article breaks down prompts into key components — instructions, primary content, examples, cues, and supporting content — explaining how each element shapes model behavior. It emphasizes that GPT models fundamentally predict the most likely next words based on training data, making prompt design more art than science. Notably, these techniques are not recommended for reasoning models like GPT-5 and o-series models.
The guide dives into practical strategies including few-shot learning, chain-of-thought prompting, output structure specification, and the use of affordances like search to ground model responses in factual data. It demonstrates how breaking complex tasks into smaller steps, repeating instructions, and using clear syntax (such as Markdown or XML separators) can significantly improve output quality. The article also covers temperature and Top_p parameters for controlling randomness, and provides grounding context techniques to reduce hallucinations.
A set of best practices rounds out the guide: be specific, be descriptive, leverage recency bias by placing instructions strategically, and give models an "out" to avoid fabricated responses. The article also addresses space efficiency, noting that tabular data formats are more token-efficient than JSON, and that careful whitespace management can help maximize the limited context window available in GPT models.
