Model Selection and Practical Prompt Engineering

With so many large language models available, choosing the right one for your application can feel overwhelming. Balancing performance, latency, and cost is critical to building successful AI-powered features. This text-based course guides you through the process of evaluating language models and writing effective prompts tailored to specific model architectures. You will learn how to make data-driven decisions when selecting models and how to optimize your prompts to achieve high accuracy while keeping operational costs low. What you'll learn: 1. Understand the core differences between major open-source and proprietary language models. 2. Evaluate model capabilities based on task complexity, context window limits, and cost constraints. 3. Apply foundational prompt engineering techniques like few-shot prompting and system instructions. 4. Optimize prompts for specific model families to maximize performance and output consistency. 5. Implement structured outputs and basic retrieval-augmented generation patterns to enhance accuracy. 6. Analyze cost-to-performance trade-offs to scale your applications efficiently. You will start with key terminology and foundational concepts of model architectures before moving into hands-on prompt design and model evaluation strategies. Through written explanations and practical code examples, you will practice comparing model outputs and refining prompts for real-world scenarios. This course is designed for beginners, developers, and product builders who want to integrate language models into their workflows, with no prior machine learning experience required. Start reading today to master the art of model selection and prompt optimization.