Llama 3 is a strong general-purpose model, but that generality becomes a drawback when an application needs an agent to call tools in a fixed JSON schema. The source argues that a model that can do many things may not reliably do one specific thing.
The tutorial presents fine-tuning with Unsloth as a remedy. By training on examples of the target API's tool calls, the model learns to output the exact structure required, improving reliability over prompting a base model.
The source is a single how-to guide, so there are no differing perspectives to compare. It focuses on the practical steps in Python, but the key insight is that task-specific fine-tuning can turn a generalist into a dependable specialist.