10 Prompting Techniques My Deep Learning Exam Taught Me (That I Actually Use Now)

In a recent article on Dev.to, a developer shares practical prompting techniques derived from academic deep learning studies, adapted for real-world software engineering. Moving beyond basic queries, the author outlines ten distinct strategies—including Few-Shot, Chain-of-Thought, and Tree-of-Thought prompting—to improve AI interactions. By applying these methods, developers can achieve more precise results in tasks like code debugging, documentation writing, and architectural decision-making. The author emphasizes that the most effective approach often involves combining these techniques, such as pairing role-playing with step-by-step reasoning. By treating AI as a collaborative tool rather than a simple search engine, developers can significantly enhance their productivity. The article serves as a concise, developer-focused guide to mastering LLM interactions, moving away from abstract textbook examples toward actionable workflows that can be immediately applied to coding projects and technical problem-solving.