A research team is developing a general-purpose chatbot. They have two options for fine-tuning their pre-trained language model:

1. Use a small, highly-curated dataset of 1,000 high-quality examples focused exclusively on customer service conversations.
2. Use a large, diverse dataset of 500,000 examples covering a wide range of topics including creative writing, coding, summarization, and general Q&A.

Analyze the likely outcome for the model's general capabilities with each approach. Explain the underlying principle that makes one strategy more suitable than the other for creating a model that can follow a wide range of instructions.