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Questions and Answers
Prompt engineering involves retraining the model's parameters to achieve task-specific performance.
False
Zero-shot prompting requires labeled data for training on specific input-output mappings.
False
Prompt engineering enables large language models to excel across diverse tasks and domains.
True
Radford et al. introduced the concept of traditional model fine-tuning in 2019.
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Zero-shot prompting is a technique that leverages the model's pre-existing knowledge to generate predictions for new tasks.
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Few-shot prompting requires no additional tokens to include the examples.
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The selection and composition of prompt examples do not influence model behavior in few-shot prompting.
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Chain-of-Thought (CoT) prompting is a technique used to prompt LLMs in a way that facilitates random and unstructured reasoning processes.
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Retrieval Augmented Generation (RAG) is a technique that requires expensive retraining of the model.
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The authors achieved an accuracy of 85.2% in math and commonsense reasoning benchmarks by utilizing CoT prompts for PaLM 540B.
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Study Notes
Prompt Engineering
- Involves designing task-specific instructions to guide model output without altering parameters
- Enables models to excel across diverse tasks and domains without retraining or extensive fine-tuning
Taxonomy of Prompt Engineering Techniques
- Organized around application domains, providing a framework for customizing prompts across diverse contexts
Zero-Shot Prompting
- Removes the need for extensive training data, relying on carefully crafted prompts to guide the model toward novel tasks
- Model receives a task description in the prompt but lacks labeled data for training on specific input-output mappings
- Model leverages pre-existing knowledge to generate predictions based on the given prompt for the new task
Few-Shot Prompting
- Provides models with a few input-output examples to induce an understanding of a given task
- Improved model performance on complex tasks compared to no demonstration
- Requires additional tokens to include examples, which may become prohibitive for longer text inputs
- Selection and composition of prompt examples can significantly influence model behavior and may still affect results
Chain-of-Thought (CoT) Prompting
- Aims to facilitate coherent and step-by-step reasoning processes in LLMs
- Proposes a technique to prompt LLMs to elicit more structured and thoughtful responses
- Demonstrates its effectiveness in eliciting more structured responses from LLMs compared to traditional prompts
- Guides LLMs through a logical reasoning chain, resulting in responses that reflect a deeper understanding of the given prompts
- Achieved state-of-the-art performance in math and commonsense reasoning benchmarks by utilizing CoT prompts for PaLM 540B, achieving an accuracy of 90.2%
Retrieval Augmented Generation (RAG)
- Seamlessly weaves information retrieval into the prompting process
- Analyzes user input, crafts a targeted query, and scours a pre-built knowledge base for relevant resources
- Retrieved snippets are incorporated into the original prompt, enriching it with contextual background
- Augmented prompt empowers the LLM to generate more accurate responses, especially in tasks demanding external knowledge
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Description
Test your knowledge of prompt engineering, a technique that enables AI models to excel across various tasks and domains without retraining. Learn how carefully crafted instructions can fine-tune model outputs, and explore the benefits of this approach over traditional model retraining. Evaluate your understanding of prompt engineering principles and applications.
