LLM Decision Guide: Choosing Between Prompting, RAG, and Fine-Tuning

Developing artificial intelligence solutions requires selecting the right customization strategy to balance performance, complexity, and budget. Knowing whether to write a better prompt, connect an external knowledge base, or retrain a model is a critical skill for modern developers and product owners. This text-only course provides a clear, conceptual framework to evaluate prompt engineering, Retrieval-Augmented Generation (RAG), and fine-tuning. You will learn how to analyze your business requirements, estimate computational costs, and choose the most effective path for your specific use case. What you'll learn: Understand the foundational differences between in-context learning, external data retrieval, and parametric model updates; Evaluate use cases to determine when simple prompt engineering is sufficient; Analyze Retrieval-Augmented Generation architectures, including vector databases and hybrid search; Explore fine-tuning concepts, including parameter-efficient methods like LoRA, and when they are necessary; Compare implementation costs, latency trade-offs, and maintenance overhead for each approach; Apply a step-by-step decision matrix to choose the optimal strategy for real-world projects. The curriculum begins with the core mechanics of large language models before guiding you through structured comparisons of prompting, RAG, and fine-tuning. You will read through detailed architectural breakdowns, trade-off analyses, and practical decision scenarios. Designed for beginners, developers, and product managers looking to build AI applications, this course requires no prior machine learning experience. Start reading today to make informed, cost-effective decisions for your next AI project.