Certified Prompt Engineering Book of Knowledge
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Questions and Answers

What is the process of designing, testing, and optimizing prompts to elicit specific responses from natural language models?

Prompt engineering

What are some principles of prompt design?

  • Use appropriate format, style, and tone to convey the prompt and the response. (correct)
  • Define the purpose, scope, and criteria of the prompt and the response. (correct)
  • Use simple, direct, and specific language and avoid jargon, slang, or idioms. (correct)
  • Provide context, background, and examples to guide the model and the user. (correct)

    • What are some ethical issues in prompt engineering?

  • The potential for bias, discrimination, or harm in the prompts and the responses, due to the data, the model, or the user. (correct)
  • The responsibility, accountability, and transparency of the prompt engineers and the model developers for the prompts and the responses. (correct)
  • The privacy, security, and consent of the users and the data providers for the prompts and the responses. (correct)
  • The quality, reliability, and validity of the prompts and the responses, and the risks and uncertainties associated with them. (correct)

    • What are some common types of language model architectures?

    <p>N-gram models</p> Signup and view all the answers

    What does the language model architecture determine?

    <p>How the language model processes and represents natural language data, and how it learns from the data and generates or analyzes the texts.</p> Signup and view all the answers

    Transformer models can only process small amounts of data.

    <p>False</p> Signup and view all the answers

    What are the common training methods for language models?

    <p>Maximum likelihood estimation</p> Signup and view all the answers

    What does language model evaluation refer to?

    <p>The measurement and assessment of the quality and performance of the language model, according to some criteria and metrics.</p> Signup and view all the answers

    What are the different ways to evaluate language models?

    <p>Intrinsic evaluation</p> Signup and view all the answers

    What are some challenges and limitations that need to be addressed and overcome in language models?

    <p>Computational cost and efficiency</p> Signup and view all the answers

    What does GPT stand for?

    <p>Generative Pre-trained Transformer</p> Signup and view all the answers

    What are some features and benefits of GPT and similar models?

    <p>They can leverage the large amounts of unlabeled text data available on the web, and learn general linguistic knowledge and patterns from them. They can capture long-range dependencies and complex relationships among words and sentences, using attention mechanisms and positional embeddings. They can generate coherent and fluent texts, using a left-to-right decoder that predicts the next word or token given the previous ones. They can adapt to different domains and tasks, using transfer learning and fine tuning techniques that adjust the model parameters according to the specific data and objective.</p> Signup and view all the answers

    What is the GPT architecture based on?

    <p>The transformer decoder</p> Signup and view all the answers

    What are the two stages of GPT training?

    <p>Fine-tuning</p> Signup and view all the answers

    The objective of the pre-training stage is to maximize the likelihood of the next token in the sequence, given the previous tokens.

    <p>True</p> Signup and view all the answers

    The fine-tuning stage uses a self-supervised learning objective.

    <p>False</p> Signup and view all the answers

    What are some examples of common query formulation techniques?

    <p>Prefixing, Reformulating, Expanding, Reducing, Handling Constraints</p> Signup and view all the answers

    What are the common ways to handle constraints in prompt engineering?

    <p>Implicitly, Explicitly, Dynamically</p> Signup and view all the answers

    What is the process of identifying, measuring, mitigating, or preventing the biases in the query formulation or the output generation?

    <p>Addressing biases</p> Signup and view all the answers

    What are some common ways to address biases in prompts?

    <p>Avoiding</p> Signup and view all the answers

    What is the process of understanding, explaining, or evaluating the output generated by the GPT model or its variants for a given query?

    <p>Interpreting model output</p> Signup and view all the answers

    What are some common ways to interpret model output?

    <p>Evaluation</p> Signup and view all the answers

    What is prompting?

    <p>A technique that involves designing, crafting, or engineering the query or the input, or the output or the feedback, for the GPT model or its variants, to elicit, generate, or produce the desired or optimal output, for various tasks or applications.</p> Signup and view all the answers

    What are some of the common pillars of prompting?

    <p>Formatting Responses</p> Signup and view all the answers

    What does ChatGPT specialize in?

    <p>Conversational AI</p> Signup and view all the answers

    What are some of the capabilities of ChatGPT?

    <p>Using different styles, tones, or moods</p> Signup and view all the answers

    What are ChatGPT plugins?

    <p>Additional, supplementary, or complementary features, functions, or tools that extend, expand, or improve the output generation of ChatGPT.</p> Signup and view all the answers

    What are some examples of ChatGPT plugins?

    <p>Sentiment analysis</p> Signup and view all the answers

    What is Github Copilot?

    <p>A code generation tool that uses artificial intelligence to help developers write code faster, easier, and better.</p> Signup and view all the answers

    What are some capabilities of Github Copilot?

    <p>Learn from the developer's own code</p> Signup and view all the answers

    What is GPT-3?

    <p>A deep learning model that generates natural language text based on some inputs, queries, or contexts from the user, using a large-scale neural network architecture called Transformer.</p> Signup and view all the answers

    What are the capabilities of GPT-3?

    <p>Learning, adapting, or improving from feedback</p> Signup and view all the answers

    What is a technique of using natural language prompts to control the behavior, output, or style of a text model, such as a sentence, paragraph, prompt, or response, by adding meta information, such as instructions, constraints, examples, or feedback?

    <p>Meta Prompting</p> Signup and view all the answers

    What is a technique of using natural language prompts to generate a sequence of logical and coherent sentences, paragraphs, prompts, or responses, that follow a chain of thought, reasoning, or argumentation, from a given text, prompt, or query?

    <p>Chain of Thought Reasoning</p> Signup and view all the answers

    What is a technique of using natural language prompts to generate a list of items, such as words, phrases, sentences, paragraphs, prompts, or responses, that are related to a given text, prompt, or query, using a text model?

    <p>Advanced List Generation</p> Signup and view all the answers

    What is a technique of using YAML syntax to define a natural language prompt that can generate a list of items, such as words, phrases, sentences, paragraphs, prompts, or responses?

    <p>Advanced List Generation YML - Coding</p> Signup and view all the answers

    What is a technique of using JSON syntax to export the output of a natural language prompt that can generate a list of items?

    <p>Advanced List Generation - Exporting JSON - Coding</p> Signup and view all the answers

    What is a technique of using natural language prompts to generate a summary, overview, or preview, of a text, prompt, or query, using a text model, and applying advanced features?

    <p>Preview</p> Signup and view all the answers

    What is a technique of using Python code to split a long text, prompt, or query, into smaller chunks, that fit within the token limit of a text model, such as ChatGPT?

    <p>Overcoming Token Limit - ChatGPT Chunking - Coding</p> Signup and view all the answers

    What is a technique of using natural language prompts to generate a sequence of logical and coherent thoughts, questions, or hypotheses, that lead to a conclusion, solution, or answer?

    <p>Let's Think Step by Step</p> Signup and view all the answers

    What is a practice of generating a text, such as a sentence, paragraph, story, or dialogue, that matches a given role, persona, or character?

    <p>Role Prompting</p> Signup and view all the answers

    What is a practice of requesting more information, details, or clarification for a given text?

    <p>Ask for Context</p> Signup and view all the answers

    What is a practice of rephrasing, reformulating, or rewording a question?

    <p>Question Rewriting</p> Signup and view all the answers

    Study Notes

    Certified Prompt Engineering Book of Knowledge

    • This document outlines the principles and best practices of prompt engineering
    • It focuses on the use of prompts to optimize responses from large language models
    • It covers topics from introduction to prompt engineering, language models, ethical considerations, and various strategies.

    Introduction to Prompt Engineering

    • Prompt engineering is the process of designing, testing, and optimizing prompts to elicit specific responses from natural language models.
    • Prompt engineering is crucial for maximizing the effectiveness and efficiency of large language models.
    • Prompt design involves consideration of content, structure, and presentation.

    Importance of Prompt Design

    • Effective prompt design directly influences the quality and coherence of generated responses.
    • Well-designed prompts reduce complexity, redundancy, and ambiguity, making the model's response more accessible.
    • Prompt engineering significantly improves the usability and engagement of the model.
    • Prompt design ensures the prompt aligns with user goals and expectations.

    Ethical Considerations in Prompt Engineering

    • Prompt engineering is a social and ethical endeavor involving prompt engineers, users, and models.
    • Ethical concerns relate to bias, discrimination, harm, privacy, security, consent, responsibility, accountability, and transparency.
    • Prompt engineers must respect the dignity, diversity, and rights of users and data providers, and avoid causing harm or offense.
    • Transparency regarding limitations or uncertainties present in prompts or responses is equally important.

    Understanding Language Models

    • Language models are computational systems that process natural language.
    • They learn patterns, structures, and rules of language from vast datasets.
    • Architectures like N-gram models and neural network models (transformers) are common types of language models.

    Training Methods

    • Maximum likelihood estimation aims to maximize the likelihood of observed data given model parameters.
    • Maximum entropy seeks to maximize model distribution entropy based on observed data.
    • Adversarial training uses an adversary to test the robustness and diversity of a language model.

    Language Model Evaluation

    • Intrinsic evaluation directly assesses a language models' ability to fit or predict data.
    • Extrinsic evaluation indirectly measures performance on downstream tasks such as translation or summarization.
    • Human evaluation evaluates quality through tasks like fluency or relevance using subjective assessments.

    GPT and Similar Models

    • GPT (Generative Pre-trained Transformer) is a family of language models pre-trained on massive text corpora.
    • GPT models learn general linguistic knowledge and relationships between words and sentences.
    • These models can be adapted to various tasks through transfer learning and fine-tuning.

    GPT Architecture

    • The GPT architecture uses a transformer decoder, with masked self-attention and feed-forward layers.
    • Embeddings represent words, positions, and segments for input and output processing within the model.
    • A masked self-attention mechanism prevents the model from considering future tokens.

    GPT Training Process

    • GPT models are trained using two stages: pre-training and fine-tuning. Pre-training uses large text corpora, and fine-tuning focuses on specific tasks or domains.
    • The objective function for pre-training is to maximize the likelihood of the next token, using the loss function, optimization algorithm, and evaluation metrics.

    Prompt Design Techniques

    • Techniques like prefixing, reformulating, expanding, and reducing modify or adapt the query or input to manage constraints and desired outputs.

    Addressing Biases

    • Identifying, measuring, mitigating, and preventing biases is crucial to avoid negative impacts.
    • Recognizing potential for bias in data, the model, and users is vital.
    • Ensuring prompts and responses are fair, accurate, and trustworthy is also key.

    Meta LLaMA

    • Meta LLaMA is a prompt engineering model to fine-tune natural language models.
    • Meta LLaMA utilizes a meta-learning framework for optimizing prompts on new tasks and domains.

    Anthropic Claude

    • Anthropic Claude evaluates natural language prompts for text generation tasks.
    • A contrastive learning approach compares generated text with human-written references for assessment.

    Prompt Engineering Strategies

    • Prompt engineering strategies guide models to produce more desired outputs.
    • Techniques include reducing complexity, providing context, and dynamic adjustments to the prompting based on initial and continuing feedback or data changes.

    Controlled Generation

    • Controlled generation adjusts models to provide outputs matching specified or intended criteria.
    • Techniques entail manipulating the query or input, as well as providing placeholders or incorporating prefixes.

    Iterative Optimization

    • Iterative optimization involves re-evaluating and modifying a text model's outputs to improve or enhance generation qualities.

    Pillars of Prompt Engineering

    • Providing examples to clarify the expectations
    • Giving direction or guidance to manage the expected output(s)
    • Formatting responses to improve their usability and accessibility

    ChatGPT Introduction

    • ChatGPT is a specialized GPT model trained for conversational AI tasks.
    • ChatGPT generates natural language responses based on inputs/queries and contexts.
    • It adapts its responses by learning from feedback and other forms of training.

    ChatGPT Plugins

    • ChatGPT plugins extend its capabilities with features like sentiment analysis or entity recognition to perform NLP tasks.

    GitHub Copilot Introduction

    • GitHub Copilot is a code generation tool using artificial intelligence.
    • It works as an extension to Visual Studio Code, providing suggestions for code completion, refactoring, and documentation.

    GPT-3 Introduction

    • GPT-3 is a powerful large language model.
    • It can perform various NLP tasks and is pre-trained on large datasets of text data.
    • Prompts can be used in tasks like text summarization, translation, and question answering.

    Advanced Text Model Techniques

    • Meta prompting is a technique to control model behavior via auxiliary information such as context, instructions, and constraints.

    • Chain of Thought Reasoning involves using natural language prompts to produce multiple steps of logical reasoning.

    • Advanced list generation is used to create lists of items based on natural language prompts.

    • Advanced list generation (JSON-coding) involves creating JSON formatted output.

    • Preview generation is used to offer concise summaries or overviews of text.

    Standard Text Model Practices

    • Techniques like List Generation, Sentiment Analysis, and Explain It Like I'm Five (ELI5), show different kinds of prompt and interaction designs and implementations
    • Strategies for generating accurate and relevant texts in various tasks.
    • Prompting practices that ensure texts for a range of outputs.

    Applications of Prompt Engineering

    • Applications of prompt engineering include chatbots, language generation, virtual assistants, content creation, and more.

    Glossary of Terms

    A clear explanation of all the technical terminology for prompt engineering such as cues, templates, or prefixes.

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    Certified Prompt Engineering Body of Knowledge PDF

    Description

    Explore the principles and best practices of prompt engineering in this comprehensive quiz. Learn how to design and optimize prompts to improve responses from large language models. Delve into topics like ethical considerations, strategies, and the importance of prompt design for effective communication with AI.

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