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LLM Prompt Engineering Patterns

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Summary

This article discusses the concept of prompt engineering and its relevance in the AI era. It introduces a number of patterns that can be used to engineer effective prompts for large language models, such as ChatGPT. These patterns include the Scaffolding, Redirection, Multi-Step, Constraint, Act As, and Data Patterns, as well as the Fallback Pattern for managing unexpected responses. It also discusses the benefits of combining patterns and strategies for dealing with ambiguous prompts. It concludes by emphasizing the importance of mastering design patterns to achieve better AI experiences and maximize the potential of large language models.

Q&As

What is prompt engineering?
Prompt engineering is the art and science of crafting effective prompts to maximize the potential of large language models, such as ChatGPT.

What are the benefits of applying design patterns to prompt engineering?
Benefits of applying design patterns to prompt engineering include improved clarity and structure, enhanced precision, improved relevance, enhanced focus, greater control over the AI's output, enhanced interactivity, progressive refinement, flexibility, controlled output, bias mitigation, customization, versatility, engagement, and data analysis.

What are the different design patterns for prompt engineering?
The design patterns for prompt engineering include the Scaffolding Pattern, Redirection Pattern, Multi-Step Pattern, Constraint Pattern, Act As Pattern, Data Pattern, and Fallback Pattern.

How can ambiguous prompts be effectively addressed to achieve accurate results?
Strategies for effectively addressing ambiguous prompts include being specific and clear, using examples, employing constraints, leveraging design patterns, iterating and refining, experimenting with prompt phrasings, and embracing AI uncertainty.

What are the benefits of embracing design patterns for LLM Prompt Engineering?
Benefits of embracing design patterns for LLM Prompt Engineering include improved effectiveness, greater versatility, enhanced customization, adaptability, resilience, and continuous improvement.

AI Comments

👍 This is a great article that provides valuable insights on the use of design patterns in prompt engineering. It is interesting to learn the history and theory of patterns, as well as their practical applications.

👎 This article is too long and some of the examples are unnecessary and not relevant.

AI Discussion

Me: It's about prompt engineering for large language models, such as ChatGPT. It discusses the concept of design patterns in the AI era and explains how they can be applied to prompt engineering. It also proposes a number of patterns that can be used to create effective prompts.

Friend: That's really interesting. What are some of the implications of this article?

Me: Well, the article suggests that by using design patterns, it's possible to create more effective and engaging AI experiences. It also suggests that by leveraging the full potential of LLMs, it's possible to create value-driven AI interactions that are tailored to user needs and expectations. By understanding and mastering the principles of prompt engineering, it's possible to create more impactful, accurate, and relevant AI experiences.

Action items

Research and learn more about the history, theory, and practical applications of design patterns for prompt engineering.
Experiment with combining different design patterns to maximize the potential of large language models.
Practice handling ambiguous prompts by being specific and clear, using examples, employing constraints, leveraging design patterns, iterating and refining, experimenting with prompt phrasings, and embracing AI uncertainty.

Technical terms

Large Language Model (LLM): A type of artificial intelligence (AI) system that uses natural language processing (NLP) to generate human-like responses to user input.
Prompt Engineering: The art and science of crafting effective prompts to maximize the potential of large language models.
Design Patterns: A set of standardized solutions to common problems in software development, architecture, and now AI.
Scaffolding Pattern: A design pattern that improves the quality of LLM-generated responses by providing a clear structure and guiding the AI towards the desired information.
Redirection Pattern: A design pattern that helps guide AI-generated responses towards desired outcomes by refining, rephrasing, or adding constraints to prompts.
Multi-Step Pattern: A design pattern for interacting with LLMs that facilitates dynamic, conversational exchanges.
Constraint Pattern: A design pattern that helps control AI behavior and mitigate biases in LLMs by setting explicit boundaries in the prompt.
Act As Pattern: A design pattern that leverages the versatility of LLMs by instructing them to adopt alternate personalities or roles.
Data Pattern: A design pattern that leverages the capabilities of LLMs to analyse and process structured data, such as JSON or CSV.
Fallback Pattern: A design pattern that helps manage unexpected or unsatisfactory responses from LLMs.
Combining Patterns: Combining design patterns can enhance LLM interactions.