Prompt Engineering – Text Summarization


Prompt Engineering, despite its technical-sounding name, simply involves providing clear directions to a computer program for various tasks. These tasks can include summarizing text, translating languages, generating content, answering questions, analyzing data, and creating code, among others. For example, if you’re a student reading a long article or a professional looking for a quick overview of a report, using text summarization can help you get accurate and important points from the original text, without having to read the entire article. It’s like giving the computer a roadmap to follow, so that it knows exactly what you want in the summary.

There is an entire field related to working with language models which has come to be known as “prompt engineering”, but as the field has progressed its scope has outgrown merely engineering the prompt into engineering systems that use model queries as components. To learn more, read our guide on prompt engineering which covers methods to improve model reasoning, reduce the likelihood of model hallucinations, and more. You can also find many useful resources including code samples in the OpenAI Cookbook.

The OpenAI Documentation

The OpenAI documentation suggests the following six strategies for achieving better results:

The documentation explains how each of the strategies listed above can be implemented using specific tactics, which are intended to inspire ideas for experiments in the OpenAI playground.

To learn more, the documentation includes related resources from around the web to learn further, including prompting guides, video courses, and more advanced resources.

Here is an example prompt from ChatGPT for summarizing text in 200 words:

Summarize the main findings and conclusions of the document in approximately 200 words, maintaining a neutral tone and adhering closely to the original source.

OpenAI Prompt Examples

https://platform.openai.com/examples

Anthropic: Prompt Engineering Techniques

The Anthropic suggests the following prompt engineering techniques for Claude‘s performance:

  • Be clear & direct: Provide clear instructions and context to guide Claude’s responses
  • Use examples: Include examples in your prompts to illustrate the desired output format or style
  • Give Claude a role: Prime Claude to inhabit a specific role (like that of an expert) in order to increase performance for your use case
  • Use XML tags: Incorporate XML tags to structure prompts and responses for greater clarity
  • Chain prompts: Divide complex tasks into smaller, manageable steps for better results
  • Let Claude think: Encourage step-by-step thinking to improve the quality of Claude’s output
  • Prefill Claude’s response: Start Claude’s response with a few words to guide its output in the desired direction
  • Control output format: Specify the desired output format to ensure consistency and readability
  • Ask Claude for rewrites: Request revisions based on a rubric to get Claude to iterate and improve its output
  • Long context window tips: Optimize prompts that take advantage of Claude’s longer context windows

Khan Academy: Good Prompt Examples

The Khan Academy lists prompt examples that present a distinct challenge and motivate the AI to generate relevant and effective outputs.

  • Scenario-based prompt. “Imagine you are a time traveler from the 28th century. Describe the technological advancements that exist in your time.”
  • Persuasive prompts. “Write an argument in favor of renewable energy sources compared to fossil fuels, highlighting their long-term sustainability and overall benefits.”
  • Creative prompts. Create a short story about a robot that feels human emotions and has to navigate the complexities of emotional intelligence.”
  • Informative prompts. “Explain the process of photosynthesis in simple terms.”
  • Discussion prompt. “Discuss the ethical implications of artificial intelligence in decision-making roles.”

ChatGPT Prompt Examples

I asked ChatGPT to provide me with five example prompts for summarizing a piece of text in about 200 words, maintaining a neutral tone and fidelity to the original text. Here the responses from the ChatGPT:

  1. Can you give a short summary of the main points from the document? Aim for around 200 words, and try to keep it neutral, like the original.”
  2. In your own words, could you summarize the most important findings and conclusions from the document? Keep it short and to the point, around 200 words, and try to stay neutral, just like the original.
  3. Could you condense the main ideas and conclusions of the document into a brief summary? Try to keep it simple and accurate, around 200 words, and maintain the same neutral tone as the original text.
  4. Can you provide a short summary of the document’s main points? Try to keep it clear and concise, around 200 words, and make sure it reflects the original content accurately.
  5. Could you summarize the key findings and conclusions of the document in your own words? Keep it short, around 200 words, and try to stay neutral, just like the original.

Wrapping Up

Prompt Engineering is a powerful tool for anyone looking to efficiently and accurately summarize written material. By adhering to strategies such as specificity, clarity, neutrality, iterative refinement, and feedback loop, one can craft prompts that guide computer programs to generate summaries that meet their requirements. Regardless of whether you’re a student, researcher, or professional, mastering prompt engineering can save you time and effort in digesting complex information.

Related Resource Links

Acknowledgements: ChatGPT was used to summarize some content and prepare its first draft.