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A Supervised Fine-Tuning (SFT) dataset is composed of samples, where each sample includes an input `x` and an output `y`. The input `x` typically combines an instruction with user-provided context (e.g., an article to summarize, a report to analyze). The output `y` is the desired response that correctly follows the instruction. For instance, tasks can range from summarization and financial data extraction to email classification and technical troubleshooting.

Example of SFT Dataset Samples

A machine learning engineer is preparing data to teach a language model how to summarize news articles. Which of the following examples represents a single, well-formed data sample that correctly combines an instruction, context, and a desired response for this task?

A data scientist is creating samples for a dataset to train a language model to be a helpful customer service assistant. Analyze the two data samples below. Which sample is more effective for this training purpose? Justify your reasoning by identifying at least two specific weaknesses in the less effective sample.

Evaluating SFT Data Sample Quality

You are creating a dataset to fine-tune a language model for email classification. Your task is to create one complete data sample. Given the email text below, formulate the three necessary components for a single supervised fine-tuning (SFT) data sample: 1) a clear instruction, 2) the input context (the email itself), and 3) the desired model output.

**Email Text:**
'Congratulations! You've won a free cruise to the Bahamas! Click here to claim your prize now! This is a limited-time offer, so act fast!'

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