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Synthesizing Tool Output into a Final Response
A language model is connected to an external weather tool. A user asks the model, 'Should I bring an umbrella to the park in London tomorrow?' The model determines it needs more information and generates a command to call the weather tool. The tool executes and returns the following data to the model: {"city": "London", "date": "tomorrow", "forecast": "Rain", "chance_of_precipitation": "85%"}. Based on this new information, what is the most appropriate final response the model should generate for the user? Explain the process the model follows to construct this response.
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Ch.3 Prompting - Foundations of Large Language Models
Foundations of Large Language Models
Foundations of Large Language Models Course
Computing Sciences
Application in Bloom's Taxonomy
Cognitive Psychology
Psychology
Social Science
Empirical Science
Science
Related
A user asks a language model, which has access to an external financial data tool, the following question: 'What is the current price of gold and what was the price one year ago?' Arrange the following actions in the logical order they would occur for the model to generate a complete answer.
Synthesizing Tool Output into a Final Response
A language model, fine-tuned to use an external weather tool, is asked: "What's the weather like in Paris today and what should I wear?". The model first generates a command to call the weather tool, which returns the following data:
{'temperature': '15°C', 'condition': 'Cloudy'}. What is the most crucial next step in the model's process to generate a complete and helpful final answer?