text expansion

Every Day Prompt Engineering Part 6: Text Expansion

In this instalment of the every day prompt engineering series, we’ll cover how to expand a given piece text. Text expansion refers to the process where a brief snippet of text, such as a set of instructions or a collection of topics, is elaborated into a more detailed document, like an email or an blog post, with the help of a large language model. While this feature holds numerous advantages, such as facilitating brainstorming sessions, it's important to be aware of possible negative applications, including the creation of spam. Therefore, it's vital to employ these text expansion capabilities ethically and in manners that positively impact individuals and communities.

Applications of Text Expansion

As highlighted, the scope of text expansion extends across a broad spectrum of applications, each showcasing the versatility of this tool in enhancing various forms of written communication. The applications, while diverse, share a common goal: to enrich initial, concise inputs into more elaborate and comprehensive outputs. Here's an expanded look at these applications:

  1. Content Creation: This involves transforming basic outlines or bullet points into comprehensive content pieces. Text expansion plays a crucial role in fleshing out the skeletal structure of ideas into detailed articles, engaging blog posts, or thorough reports. Writers and content creators leverage this capability to bridge the gap between initial concepts and finished content, ensuring that the end products are well-researched, informative, and reader-friendly.
  2. Creative Writing: In the realm of fiction or creative non-fiction, text expansion aids in the elaboration of simple plot summaries, character sketches, or environmental descriptions into rich narratives. This process allows writers to develop complex characters with deep backstories, intricate settings that feel alive, and plots that captivate readers. It's particularly useful for overcoming writer's block or enhancing initial ideas into full stories or novels.
  3. Educational Material: Educators and content developers can use text expansion to transform brief explanations or summaries into detailed educational resources. This includes comprehensive tutorials that guide learners step-by-step, detailed lesson plans that provide educators with a clear roadmap for teaching, and in-depth explanations that help clarify complex concepts. The goal is to make learning materials more accessible, understandable, and engaging for students of all levels.
  4. Email and Communication: In professional and personal communication, text expansion helps in crafting well-articulated emails or messages from brief initial notes or ideas. This can be especially useful for drafting business correspondence, customer support replies, or personalized messages where clarity and detail are paramount. By expanding on key points, the sender can ensure their message is understood as intended, improving the effectiveness of their communication.

Beyond these core areas, text expansion can also support:

  1. Social Media Content: Generating engaging and detailed posts from brief ideas or prompts, tailored to fit various platforms and their audience's expectations.
  2. Technical Documentation: Creating detailed guides, manuals, and documentation from concise technical specifications or outlines, making complex information accessible to users.
  3. Scriptwriting and Screenplays: Expanding story ideas or scene summaries into full scripts, complete with dialogue, stage directions, and character interactions.
  4. Marketing and Advertising Copy: Transforming short product descriptions or features into compelling marketing narratives that highlight benefits, engage potential customers, and drive action.
  5. Research and Summarization: Taking key points from research or data and expanding them into comprehensive analyses, reports, or summaries that provide deeper insights.
  6. Language Learning: Assisting in the creation of language learning materials by expanding simple sentences into conversations or explanatory texts, aiding in contextual learning and comprehension.

Each of these applications underscores the transformative power of text expansion, not just in creating content, but in enriching communication, fostering creativity, and enhancing learning experiences.

How to Leverage LLMs for Text Expansion

Using text expansion itself is straightforward, all you have to do is to tell the LLM to do it. Here’s an example:

Expand the following text. text = “””<text>”””

A typical use case for text expansion is the creation of blog articles that we’ll go through in detail. To create a blog article, you start with the outline of the article and then iteratively go over every section step by step. Note that this is a typical approach when prompting: you start with some initial prompt and keep on iterating over the results. Let’s break our blog article down into several prompts:

1. Create the outline of the blog post

You are tech writer with deep knowledge about prompt engineering. Create a detailed outline for a blog post about prompt engineering. Work the problem step by step and take a deep breath before you begin.

This prompt does several things. First, it asks the LLM to act as persona. This primes the LLM to answer as if it is the persona. This role playing leads to better results, because you tell the LLM how to act. Then, the prompt asks for an outline of a blog post, which is what you want. The prompt also uses a technique called chain of thought that nudges the LLM towards a “structured thought process”. Finally, giving the LLM time to think also gives you better results. The result of this prompt is a nicely written outline:

Creating a comprehensive blog post on prompt engineering requires a structured approach to cover the breadth of this topic effectively. Here is a detailed outline that encompasses the key aspects of prompt engineering, designed to educate and engage readers:

### Introduction to Prompt Engineering
- **Definition and Overview**: Explain what prompt engineering is and its relevance in the AI and machine learning landscape.
- **Historical Context**: Briefly touch on the evolution of AI models and how prompt engineering became a crucial aspect of leveraging these models.

### The Importance of Prompt Engineering
- **Enhancing Model Performance**: Discuss how prompt engineering can significantly improve the performance of AI models in tasks like text generation, image creation, and data analysis.
- **Customization and Flexibility**: Illustrate how prompts can be tailored to meet specific user needs or project requirements.

### Fundamentals of Prompt Engineering
- **Understanding AI Models**: A brief overview of how AI models like GPT (Generative Pre-trained Transformer) work.
- **The Role of Prompts**: Explain how prompts serve as inputs to AI models and influence their outputs.
- **Types of Prompts**: Introduce different types of prompts (e.g., open-ended, targeted, instructive) and their use cases.

### Strategies for Effective Prompt Engineering
- **Best Practices**: Share best practices for crafting prompts that yield high-quality responses from AI models.
- **Experimentation and Iteration**: Discuss the importance of experimenting with different prompts and iterating based on results.
- **Understanding Model Limitations**: Insight into the limitations of AI models and how they impact prompt engineering strategies.

### Advanced Prompt Engineering Techniques
- **Prompt Programming**: Introduce the concept of prompt programming, including techniques like zero-shot, few-shot, and chain-of-thought prompting.
- **Tailoring Prompts for Specific Domains**: Tips for creating prompts for specialized areas (e.g., legal, medical, creative writing).
- **Prompt Templates and Libraries**: Overview of resources available for prompt engineering, including templates and prompt libraries.

### Practical Applications of Prompt Engineering
- **Case Studies**: Present real-world examples of how prompt engineering has been applied in various industries (e.g., content creation, customer service, research).
- **Innovative Uses**: Explore innovative and unexpected applications of prompt engineering.

### Challenges and Ethical Considerations
- **Bias and Fairness**: Discuss how prompts can inadvertently introduce or amplify biases in AI responses.
- **Mitigating Risks**: Strategies for mitigating these risks and promoting ethical use of AI through careful prompt engineering.
- **Privacy and Security**: Considerations regarding the use of sensitive information in prompts.

### The Future of Prompt Engineering
- **Emerging Trends**: Speculate on future developments in prompt engineering and AI model interaction.
- **The Role of Community and Open Source**: Highlight the importance of community contributions and open-source projects in advancing prompt engineering practices.

### Conclusion
- **Recap of Key Points**: Summarize the main takeaways from the post.
- **Encouragement to Experiment**: Encourage readers to explore prompt engineering themselves, emphasizing the creative and experimental nature of this field.
- **Further Resources**: Provide links to additional resources, communities, and tools for readers who wish to delve deeper into prompt engineering.

### Call to Action
- Invite readers to share their experiences, successes, and challenges with prompt engineering in the comments or on social media to foster a community of practice.

This outline provides a comprehensive roadmap for creating an informative and engaging blog post on prompt engineering. Each section is designed to build on the previous ones, guiding the reader from a basic understanding to more complex concepts and practical applications, while also addressing ethical considerations and looking toward the future of the field.

2 Iterate over every section

After the outline is available, you simply go manually over every section and ask the LLM to expand it. Here’s an example to expand the introduction:

Expand the Introduction to Prompt Engineering.

The result can be found here.

You can also use an approach, where you ask the LLM to do an interactive process with user input at every step:

expand every section of the blog post outline in an interactive process. after completing a section, you ask for input from the user before you continue with the next section until you have completed the blog post.

You’ll notice that the outline is different. In case you wonder if that’s a bug: it’s not. It’s a feature of LLMs to give similar but different answers to the same prompts. This is because the models are probabilistic. This means that a LLM generates a response based on a probability distribution. If you enter a prompt, the LLM generates multiple possible responses and ranks them by likelihood. Here's the result of the second prompt:

Let's start with expanding the **Introduction** section of the blog post on prompt engineering.


### Introduction to Prompt Engineering

In the rapidly evolving landscape of artificial intelligence (AI), prompt engineering emerges as a critical yet often understated discipline. At its core, prompt engineering involves the strategic crafting of inputs designed to elicit desired responses from AI models, particularly in the realms of natural language processing (NLP) and computer vision. This nuanced art form plays a pivotal role in optimizing the utility and accuracy of AI technologies, from conversational agents like chatbots to sophisticated image generation tools.

The significance of prompt engineering has surged alongside the development of advanced AI models such as OpenAI's GPT series. These models, capable of generating text that closely mirrors human writing, depend heavily on the quality and specificity of prompts they receive. A well-engineered prompt can guide these AI systems to produce outputs ranging from coherent and contextually relevant text to stunningly accurate visual artworks. Conversely, vague or poorly constructed prompts can lead to responses that are at best irrelevant and at worst misleading or nonsensical.

As we stand on the brink of a new era in AI, where the interaction between humans and machines becomes increasingly seamless, the role of prompt engineering cannot be overstated. It bridges the gap between human intent and AI comprehension, enabling these digital entities to better serve our informational, educational, and creative needs. This blog post aims to unpack the intricacies of prompt engineering, providing insights into its mechanisms, applications, and challenges, and offering guidance for those looking to master this essential skill.


Before proceeding to the next section, which will dive into the understanding of prompt engineering, do you have any specific areas within this introduction that you would like to expand further or any feedback to incorporate?

After the LLM completes a section, it asks for input. Let’s say you are not satisfied with the part about the historical context and want more details. You can ask to elaborate the section and do a web research.

elaborate the historical context and include historical details. do a web research.

This gives you additional details for the section in question.

Limits of interactive prompting

If you take a look at the chat history you’ll notice that the LLM kind of took a shortcut and didn’t go through all the sections. Rather, it swiftly completed the task. This is commonly referred to as laziness. To overcome this limitation and to keep the model on track, you can break down the overall task into smaller interactive subtasks:

You are tech writer with deep knowledge about LLMs. expand the first three sections of the blog post outline in an interactive process. after completing a section, you ask for input from the user before you continue with the next section. outline = """Title: The Art and Science of Prompt Engineering: Unleashing the Power of AI
Define prompt engineering.
Brief overview of its importance in AI applications.
Mention the rise of language models like GPT-3 and their reliance on effective prompting.
Section 1: Understanding Prompt Engineering
Definition and Scope
Clarify what prompt engineering is.
Differentiate between prompt engineering for text and image-based AI models.
Historical Context
Trace the evolution of prompt engineering alongside AI development.
Mention key milestones.
Section 2: The Role of Prompt Engineering in AI
Enhancing AI Performance
Examples of how prompt engineering improves AI responses.
Case studies or references to notable successes.
Applications Across Industries
Content creation, coding, data analysis, and more.
Specific use cases in healthcare, finance, and creative industries.
Section 3: Challenges in Prompt Engineering
Understanding Model Limitations
Discuss the constraints of AI models and how they affect prompting.
Ethical Considerations
Address potential biases in AI responses.
The importance of crafting prompts that mitigate ethical risks.
Complexity of Crafting Effective Prompts
The trial-and-error nature of prompt engineering.
The role of domain expertise in crafting prompts.
Section 4: Best Practices in Prompt Engineering
Know Your AI Model
Understanding the capabilities and limitations of your AI model.
Clarity and Specificity
The importance of being clear and specific in your prompts.
Iterative Refinement
Techniques for refining prompts based on AI responses.
Collaboration and Sharing
The role of community in sharing effective prompts and strategies.
Section 5: Tools and Technologies
Software and Platforms
Overview of tools designed to assist with prompt engineering.
Integration with AI Services
How prompt engineering fits into broader AI ecosystems and services.
Section 6: Future Directions in Prompt Engineering
Advancements in AI Models
Anticipated developments in AI that could impact prompt engineering.
Automated Prompt Engineering
The potential for AI to assist in its own prompt optimization.
Ethical and Societal Implications
Future considerations for the responsible use of prompt engineering.
Recap the significance of prompt engineering in maximizing the potential of AI technologies.
Encourage readers to explore and contribute to the field.
Call to Action
Invite readers to share their experiences and tips on prompt engineering.
Suggest forums, communities, and resources for further exploration.
Additional Resources
List of articles, books, and online resources for readers who want to delve deeper into prompt engineering.
This outline provides a comprehensive framework for a blog post on prompt engineering, ensuring that readers come away with a clear understanding of what it is, why it's important, the challenges it faces, and how to approach it effectively."""

Breaking a large task into smaller subtasks is a fundamental strategy in prompt engineering for AI systems. This approach is called decomposition and it helps to simplify complex problems into smaller, more manageable ones. By breaking down a large task into smaller subtasks, it becomes easier to identify and solve problems, and it also makes the overall process more efficient and less error-prone.
As shown in the example, in prompt engineering, decomposition is used to create a set of smaller, more focused prompts that can be used to generate more specific responses.


Text expansion through LLMs like ChatGPT opens up a world of possibilities for content creation, education, and communication. By understanding how to effectively craft prompts and guide these models, you can harness their full potential to create detailed, engaging, and informative text. As you experiment with text expansion, remember that the key lies in clear objectives, detailed prompts, and a willingness to iterate. With these tools at your disposal, the only limit is your imagination.

Every Day Prompt Engineering Part 5: Text Analysis

Everyday Prompt Engineering Part Four: Document and Text Summaries

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