Designing Prompt Templates for DeepSeek and Phi LLMs

Harnessing the full capabilities of modern language models requires more than basic conversational inputs. This text-based guide teaches you how to design robust, modular prompt templates tailored for open-weight models such as DeepSeek and Phi. You will transition from basic querying to engineering structured prompt pipelines that deliver consistent, accurate, and reproducible results for diverse real-world tasks. What you'll learn: - Understand core architecture fundamentals and prompt processing in modern open-weight models - Design reusable prompt templates using system instructions, contextual framing, and role assignments - Apply few-shot learning and structured output formatting techniques to guide model reasoning - Integrate retrieval-augmented context and dynamic variables into modular template workflows - Test, evaluate, and refine prompt variations to eliminate ambiguity and prevent hallucinations The training begins with foundational concepts and terminology before advancing through systematic prompt design patterns and written implementation exercises. Designed for developers, analysts, and AI enthusiasts of all skill levels, this course requires no previous machine learning background. Start reading today to build reliable, high-performance prompt architectures for modern language models.