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The AI Builder’s Toolkit: When to Use RAG, Fine-Tuning, and Prompt Engineering
Every AI product builder faces the same critical decision: How do you make your language model truly useful for your specific use case? The answer isn’t found in chasing the latest AI trend or adopting every shiny new tool. It lies in understanding three fundamental approaches, each with distinct strengths and trade-offs.
Think of it like choosing the right tool for home renovation. You wouldn’t use a sledgehammer to hang a picture frame, nor would you use a finishing nail to demolish a wall. The same principle applies to AI development. RAG, fine-tuning, and prompt engineering each serve different purposes, and knowing when to use which combination can make or break your AI product.
Prompt Engineering: The Foundation Everyone Needs
“Prompt engineering is just prompting these days,” as my upcoming podcast guest Hamel Husain puts it. While this might sound dismissive, it captures an important truth: prompting has become so fundamental that we sometimes forget how powerful sophisticated prompt engineering can be.
At its core, prompt engineering is about better activating a model’s existing capabilities. It’s the art of crafting inputs that guide the model toward the outputs you need. But when we talk about…
