A development team is fine-tuning a language model to answer questions about recent events by using a search_web(query) tool. Their training dataset consists of prompts paired with complete, fact-based answers. For example:

Prompt: 'Who won the Best Picture award at the most recent Oscars?' Target Output: 'Oppenheimer won the Best Picture award at the most recent Oscars.'

After fine-tuning on thousands of similar examples, the team observes that the model still frequently provides incorrect or made-up answers for new questions about recent events, instead of learning to use the search tool. Based on the principles of preparing data for tool use, diagnose the fundamental error in the team's data preparation strategy and explain what the correct target output should have looked like for the given prompt.