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Questions and Answers
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?
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What is the primary goal of Chain of Thought prompting?
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What is the benefit of using self-consistency with Chain of Thought prompts?
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What is Inception known as?
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What is the primary difference between Chain of Thought and Inception?
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What is the main purpose of prompt engineering?
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Study Notes
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
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Description
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).
