Prompt Engineering Quiz: Test Your Fundamentals

What is the fundamental aspect that allows building software applications with LLMs?

How do we use a prompt with a LLM?

What is the purpose of providing additional context to the LLM in few-shot learning?

What is the technique where we provide a cultural reference or an analogy for the LLM to understand what it needs to do?

What is the primary goal of Chain of Thought prompting?

What is the benefit of using self-consistency with Chain of Thought prompts?

What is Inception known as?

What is the primary difference between Chain of Thought and Inception?

What is the main purpose of prompt engineering?

Prompt Engineering Fundamentals

Prompt engineering is a crucial aspect of building software applications with Large Language Models (LLMs)
A prompt is input into an LLM, which processes it to produce an output, similar to a function in programming where the prompt is the input variable and the output is the result

Few-Shot Learning

Few-shot learning provides additional context to the LLM in the form of examples
This technique enables the LLM to learn from a limited number of examples

Memetic Proxy

Memetic Proxy is a technique that uses cultural references or analogies to help the LLM understand what it needs to do
This approach allows the LLM to process the prompt in a more nuanced and human-like way

Chain of Thought

Chain of Thought is a few-shot technique that requires the LLM to provide a rationale for its answer
This approach forces the LLM to think step-by-step and justify its response

Self-Consistency

Self-consistency is a technique used to ensure the LLM provides consistent answers to the same question
This is achieved by running multiple Chain of Thought prompts with the same question and choosing the answer that appears most frequently

Inception

Inception is a zero-shot version of Chain of Thought
This approach does not require any examples or context, but still elicits reasoning from the LLM

Test your understanding of the fundamental concepts of prompt engineering, including elements of a prompt, few-shot learning, and other techniques used to build software applications with Large Language Models (LLMs).