18 Questions
Match the following terms with their descriptions:
Prompt Engineering = The fundamental aspect of building software applications with LLMs Few-shot learning = A technique where we provide a cultural reference for the LLM to understand Memetic Proxy = Providing additional context to the LLM in the form of examples Chain of Thought = Forcing the LLM to provide a rationale for its answer
Match the following concepts with their characteristics:
LLM = A function that modifies the input variable Prompt = An input variable for the LLM Output = The result of the LLM's modification Function = A programming construct used to build software applications
Match the following terms with their purposes:
Self-Consistency = To ensure the model gives consistent responses to the same question Inception = To force the LLM to provide a rationale for its answer Chain of Thought = To provide additional context to the LLM in the form of examples Few-shot learning = To build software applications with LLMs
Match the following terms with their relationships:
Inception = A type of zero-shot Chain of Thought Chain of Thought = A type of few-shot learning technique Few-shot learning = A technique used in prompt engineering Memetic Proxy = A type of self-consistency technique
Match the following concepts with their descriptions:
Plan and Execute = A technique used in prompt engineering for LLMs React = A technique used in prompt engineering for LLMs Self-ask = A technique used in prompt engineering for LLMs Elements of a Prompt = A fundamental aspect of building software applications with LLMs
Match the following terms with their characteristics:
LLM = A type of function that takes input and provides output Prompt = A type of input variable for the LLM Output = A type of result of the LLM's modification Function = A type of programming construct used in few-shot learning
Match the following terms with their purposes:
Inception = To provide a cultural reference for the LLM to understand Self-Consistency = To force the LLM to provide a rationale for its answer Chain of Thought = To ensure the model gives consistent responses to the same question Memetic Proxy = To build software applications with LLMs
Match the following terms with their relationships:
Prompt Engineering = A fundamental aspect of few-shot learning Few-shot learning = A technique used in Chain of Thought Chain of Thought = A type of prompt engineering technique Memetic Proxy = A type of self-consistency technique
Match the following concepts with their descriptions:
Plan and Execute = A technique used in prompt engineering to build software applications React = A technique used in prompt engineering to provide a rationale for the answer Self-ask = A technique used in prompt engineering to provide additional context Elements of a Prompt = A technique used in prompt engineering to ensure self-consistency
What is the role of a prompt in a software application using a Large Language Model?
To serve as an input variable
What is the primary goal of few-shot learning?
To provide additional context to the LLM in the form of examples
What is the purpose of the Memetic Proxy technique?
To provide a cultural reference or an analogy for the LLM
What is the primary goal of the Chain of Thought technique?
To force the LLM to provide a rationale for its answer
What is the purpose of the Self-Consistency technique?
To run multiple times a Chain of Thought prompt with the same question and choose the answer that comes up most often
What is Inception known as?
The zero-shot Chain of Thought
What is the primary difference between Chain of Thought and Self-Consistency techniques?
One forces the LLM to provide a rationale and the other chooses the most frequent answer
What is the primary advantage of using prompt engineering in software applications?
It allows for building software applications with LLMs
What is the relationship between a prompt and an LLM in a software application?
The prompt is the input variable and the LLM is the function
Study Notes
Prompt Engineering Fundamentals
- Prompt engineering is a fundamental aspect that enables building software applications with Large Language Models (LLMs)
- In prompt engineering, a prompt is input into an LLM, and an output is generated, similar to a function in programming
Few-Shot Learning
- Few-shot learning involves providing additional context to an LLM in the form of examples
- This technique enables LLMs to learn from limited data
Memetic Proxy
- Memetic Proxy is a technique that provides a cultural reference or analogy for an LLM to understand what it needs to do
- This technique is used in Prompt Programming for Large Language Models
Chain of Thought
- Chain of Thought is a few-shot technique that forces an LLM to provide a rationale for its answer
- This technique elicits reasoning in Large Language Models
Self-Consistency
- Self-Consistency involves running multiple Chain of Thought prompts with the same question and choosing the answer that comes up most often
- This technique is used to overcome the issue of an LLM providing different responses to the same question
Inception
- Inception is a zero-shot Chain of Thought technique
- It is a type of Chain of Thought prompting that does not require additional context or examples
Learn about the basics of prompt engineering, a crucial aspect of building software applications with Large Language Models (LLMs). Discover how prompts are used as input variables to generate outputs, similar to functions in programming.
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