Techniques to Improve Model Output (20%)
Overview
KEY CONCEPTS
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WHAT THE EXAM IS REALLY TESTING
COMMON TRAPS
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OFFICIAL DOCUMENTATION
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STUDY Q&A
- What is prompt tuning and how does it differ from fine-tuning?Prompt tuning involves optimizing the input prompts to guide the model's output, while fine-tuning adjusts the model's internal parameters using additional training data. Prompt tuning is less resource-intensive and does not require retraining the model.
- What is zero-shot prompting?Zero-shot prompting is a technique where the model is given a task without any example. It is suitable for simple tasks where the model can generalize from its pre-training.
- What is few-shot prompting and when is it preferable?Few-shot prompting involves providing the model with a few examples to guide its output. It is preferable when you need consistent formatting or when the task is ambiguous without context.
- What is chaining and why is it useful in complex workflows?Chaining refers to the sequential use of multiple prompts, where the output of one prompt becomes the input for the next. This is useful for decomposing complex tasks into manageable steps.
- What is Chain of Thought prompting and when should it be used?Chain of Thought prompting encourages the model to reason step-by-step, making it suitable for tasks that require logical reasoning or multi-step problem solving.