Certified Prompt Engineering Book of Knowledge

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.

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.