LLM Prompt Engineering Patterns

Prompt engineering is the art and science of crafting effective prompts to maximize the potential of large language models, such as ChatGPT. The concept of design patterns, originating in architecture and later adopted in software development, is now relevant in the AI era for prompt engineering. It is interesting to learn the history, theory, and practical applications of patterns, from industry examples and expert insights, inspiring you to create innovative prompts for AI models.

You could discover hundreds examples of Prompts on multiple websites, many of which are just replicas of the same pattern, or you could learn the patterns of creating prompts and engineer your own.

Below I propose a number of patterns that can be applied to Prompt Engineering. I welcome hearing the thoughts of others.

Scaffolding Pattern

The Scaffolding Pattern is a design pattern that improves the quality of Large Language Model (LLM)-generated responses by providing a clear structure and guiding the AI towards the desired information. It involves breaking down a prompt into smaller, more focused questions or instructions, which helps maintain focus and coherence in the AI's response. Benefits of the Scaffolding Pattern include improved clarity and structure, enhanced precision, and reduced ambiguity.

The Scaffolding Pattern is effective in generating well-structured and informative responses. By breaking down prompts into smaller, focused components, you can guide the AI more effectively towards the desired outcome, making your experiences with LLMs more fruitful and productive.

Redirection Pattern

The Redirection Pattern is a design pattern that helps guide AI-generated responses towards desired outcomes by refining, rephrasing, or adding constraints to prompts. This improves relevance, enhances focus, and provides greater control over the AI's output. It's useful for steering the AI away from off-topic, vague, or unsatisfactory responses. The Redirection Pattern is a valuable tool in a prompt engineer's toolkit, ensuring AI-generated content aligns with user needs and expectations.

Multi-Step Pattern

The Multi-Step Pattern is a design pattern for interacting with Large Language Models (LLMs) that facilitates dynamic, conversational exchanges. It involves a series of prompts and responses, emulating natural conversation, where each prompt builds upon previous responses. This pattern offers several benefits, including enhanced interactivity, progressive refinement, and flexibility. Examples of the Multi-Step Pattern in action include progressively exploring subtopics, like mobile responsiveness in website user experience, or obtaining more detailed information, such as building a reading list on renewable energy. Overall, the Multi-Step Pattern enables more effective, engaging, and adaptable interactions with AI.

Constraint Pattern

The Constraint Pattern is a design pattern that helps control AI behavior and mitigate biases in Large Language Models by setting explicit boundaries in the prompt. It provides controlled output, bias mitigation, and customization. By specifying instructions or limitations, you can ensure the AI-generated content aligns with your requirements and ethical guidelines. Applying the Constraint Pattern results in more responsible and satisfying interactions with AI, making it an essential part of any prompt engineer's toolkit.

Act As Pattern

The Act As Pattern is a design pattern that leverages the versatility of Large Language Models (LLMs) by instructing them to adopt alternate personalities or roles. This pattern offers benefits such as versatility, engagement, and customization. By explicitly instructing the AI to assume a specific identity, users can create engaging and diverse interactions that cater to various use cases and creative goals. Examples include adopting the role of a life coach, job interviewer, or even historical characters like Leonardo da Vinci. The Act As Pattern enables users to unlock the full potential of LLMs and elevate AI experiences.

Data Pattern

The Data Pattern is a design pattern that leverages the capabilities of Large Language Models (LLMs) to analyse and process structured data, such as JSON or CSV. By providing structured data and a specific prompt, the Data Pattern allows the AI to offer insights, visualisation advice, and generate code for visual representations. Key benefits of the Data Pattern include enhanced data analysis, visualisation advice, and code generation, making it an invaluable tool in the prompt engineer's toolkit. It helps streamline the data analysis process, improve data comprehension, and save time and effort, ultimately unlocking the full potential of LLMs in data analysis and visualisation.

Fallback Pattern

The Fallback Pattern is a design pattern that helps you manage unexpected or unsatisfactory responses from Large Language Models (LLMs). It involves using alternative approaches or contingency plans when the AI's output doesn't meet your expectations. This can include rephrasing prompts, providing more context or constraints, using the Multi-Step Pattern, or even switching to a different AI model or solution. Benefits of the Fallback Pattern include adaptability, resilience, and continuous improvement. By employing the Fallback Pattern, you can maintain control, adapt to unforeseen challenges, and refine your strategies when working with LLMs.

Combining Patterns

Combining design patterns can enhance LLM interactions. Benefits of combining patterns include improved effectiveness, greater versatility, and enhanced customization. Combining patterns allows you to leverage multiple aspects of the AI's capabilities for more robust and effective prompts. For example, combining the Constraint and Act As Patterns results in AI-generated advice that is role-specific and grounded in the set boundaries. Experimenting with pattern combinations is essential for prompt engineers, as it helps maximize LLM capabilities and create better outcomes.

Handling Ambiguous Prompts

When working with LLMs, it's vital to address ambiguous prompts to achieve accurate and relevant results. Strategies include being specific and clear, using examples, employing constraints, leveraging design patterns, iterating and refining, experimenting with prompt phrasings, and embracing AI uncertainty. By mastering these strategies, you can better utilize LLMs and create effective AI interactions.

Conclusion

Design patterns for LLM Prompt Engineering offer valuable insights for enhancing AI interactions. Learning from the rich history of design patterns, we can achieve a great deal by mastering core patterns, combining patterns, and learning to deal with uncertainty. Embracing design patterns enables better AI experiences, fosters innovation, and ensures the effective use of an LLM’s full potential. By engaging in dialogue and sharing knowledge, we can collectively create more impactful and engaging LLM outcomes and experiences, delivering higher value.