I am presenting a workshop as an invited presenter for a one-hour workshop on “How to be an effective Prompt Engineer in Academia”. I was not sure how to approach this topic in a way that would be easy to understand for the novice AI user, so I decided to curate the top 21 (well, 20) articles using Google’s search function (yes I am not of the TikTok generation and Youtube doesn’t let you copy-paste text). My process was:
- Search using the key words effective prompt engineering.
- Scan and copy all relevant body text from the top 21 articles on the topic.
- create a Word document of these 55,975 words using unformatted text and eliminating any artifacts of images, links, etc.
- Save this as a PDF.
- Download Microsoft Edge with Bing Chat integrated within the application.
- Open the Edge Application and search for Bing Chat
- When the Bing Chat prompt box shows up on the side panel of the application, upload the document and enter the prompt as follows: “This document includes 21 articles curated from google using a search “effective prompt engineering”. Each article is indicated with a source link at the end of the article in the format: Source: (link). The objective is to summarize and produce a consistent guide to effective prompt engineering for an academic. Use the content of these documents to create the 21 most relevant and useful tips for better prompts. For each tip, include a summary of why that tip contrubutes to effective prompt creation and provid a simple example that the reader can use within chatgpt to reinforce the utility of the tip. Use the following four examples for any of the applications of the 21 tips: 1. creating a clear understanding of how to conjugate a french verb; 2. developing a scientific A/B test to determine the market’s favorite cola; 3. crafting a professional academic abstract summarizing this entire collection of documents; 4. building a schedule of learning to master this collection of documents over the course of an 8 week class that meets monday and wednesday from 9:30-10:45 AM starting January 16 and ending March 25. The summary should take the form of a step by step application of the 21 best tips and should integrate all four of the suggested applications. The list must include 21 tips and each must be written in a way a novice ai-user would understand. Limit the response to 2000 words.”
- Press Return.Then the AI did its magic. However, this was not without its defects, both by the user (mostly spelling and grammar) and the AI (hey, it’s 165 pages of information to synthesize into a simple list), and this is a regular part of the user-AI relationship. First: there were only 20 sources to interpret, not 21. Second, the AI was limited in the length of the response and I asked why it only provided 10 tips instead of the requisite 21 tips?So I told the AI: “That was only 10 tips and not 21” to which the AI replied: “I apologize for the inconvenience. I was trying to provide you with a comprehensive summary of the 21 articles, but I ran out of space in my response. The limit is 2000 words, and I have already used 1999 words. If you want to see the rest of the tips, you can either ask me to send them in a separate message, or you can adjust the word limit in the chat settings. Thank you for your understanding. ?” How cute is that?
I replied to the affirmative to complete the list and it most definitely did! So here are the 21 top tips to be an effective prompt engineer in academia from the top 20 articles on the topic as curated from a google search, which I intend to access and provide to the workshop attendees, including the four distinct academic applications I requested:
THE TOP 21 TIPS TO BE AN EFFECTIVE AI PROMPT ENGINEER IN ACADEMIA
(FOR THE NOVICE AI LEARNER)*- Define your goal and desired outcome. Before you interact with a language model, you should have a clear idea of what you want to achieve and what kind of output you expect. This will help you formulate your prompt more effectively and evaluate the results more easily. For example, if your goal is to create a clear understanding of how to conjugate a French verb, you should specify which verb, tense, and mood you want to learn, and what kind of explanation or exercise you prefer.
- Use natural and direct language. Language models are trained on large amounts of natural language data, so they can understand and generate prompts that use simple and straightforward language. Avoid using jargon, technical terms, or ambiguous words that might confuse the model or lead to unwanted outputs. For example, if you want to develop a scientific A/B test to determine the market’s favorite cola, you should use clear and specific terms like “cola preference”, “sample size”, “hypothesis”, and “statistical significance”.
- Provide context and background information. Language models can benefit from additional information that helps them understand the prompt and the task better. You can provide context by giving examples, explanations, references, or scenarios that relate to the prompt. This can improve the relevance and accuracy of the output. For example, if you want to craft a professional academic abstract summarizing this entire collection of documents, you should provide some context about the topic, the purpose, and the main findings of the documents.
- Set constraints and expectations. Language models can generate a variety of outputs based on the prompt, but not all of them might be suitable for your needs. You can set constraints and expectations by specifying the format, style, tone, length, or quality of the output. This can help the model narrow down the possible outputs and generate more appropriate ones. For example, if you want to build a schedule of learning to master this collection of documents over the course of an 8 week class that meets Monday and Wednesday from 9:30-10:45 AM starting January 16 and ending March 25, you should specify the number of sessions, the duration, the topics, and the learning objectives of each session.
- Use keywords and phrases. Language models can recognize and generate keywords and phrases that are relevant to the prompt and the task. You can use keywords and phrases to guide the model’s attention and focus on the most important aspects of the prompt. This can improve the coherence and specificity of the output. For example, if you want to create a clear understanding of how to conjugate a French verb, you can use keywords and phrases like “subject pronouns”, “verb endings”, “regular verbs”, and “irregular verbs”.
- Use examples and templates. Language models can learn from and generate examples and templates that match the prompt and the task. You can use examples and templates to show the model what kind of output you are looking for and how to structure it. This can improve the consistency and quality of the output. For example, if you want to develop a scientific A/B test to determine the market’s favorite cola, you can use an example or a template of a similar experiment that follows the scientific method and includes the necessary components like introduction, methods, results, and conclusion.
- Use feedback and iteration. Language models can improve their outputs based on feedback and iteration. You can use feedback and iteration to correct errors, clarify ambiguities, or refine details in the output. This can improve the accuracy and completeness of the output. For example, if you want to craft a professional academic abstract summarizing this entire collection of documents, you can use feedback and iteration to check the grammar, spelling, and logic of the output, and to add or remove information as needed.
- Use different models and perspectives. Language models can have different strengths and weaknesses depending on their architecture, training data, and parameters. You can use different models and perspectives to compare and contrast the outputs and to explore different angles and approaches to the prompt and the task. This can improve the diversity and creativity of the output. For example, if you want to build a schedule of learning to master this collection of documents over the course of an 8 week class that meets Monday and Wednesday from 9:30-10:45 AM starting January 16 and ending March 25, you can use different models and perspectives to generate different schedules that vary in the order, depth, and difficulty of the topics.
- Use multimodal prompts and outputs. Language models can handle and generate multimodal prompts and outputs that include text, images, audio, video, or other formats. You can use multimodal prompts and outputs to enhance the interaction and the output with visual, auditory, or other stimuli. This can improve the engagement and the effectiveness of the output. For example, if you want to create a clear understanding of how to conjugate a French verb, you can use multimodal prompts and outputs to include images of verb charts, audio of verb pronunciations, or video of verb usage in sentences.
- Use zero-shot, one-shot, or few-shot prompting. Language models can generate outputs based on zero-shot, one-shot, or few-shot prompting, which means using no examples, one example, or a few examples to guide the model. You can use zero-shot, one-shot, or few-shot prompting to test the model’s generalization and adaptation abilities and to save time and resources. This can improve the efficiency and the flexibility of the output. For example, if you want to develop a scientific A/B test to determine the market’s favorite cola, you can use zero-shot, one-shot, or few-shot prompting to see how the model can design an experiment based on different levels of prior knowledge.
- Use natural language inference. Natural language inference is the ability to infer the logical relationship between two sentences, such as entailment, contradiction, or neutrality. You can use natural language inference to check the validity and consistency of the output, and to generate new outputs based on existing ones. For example, if you want to create a clear understanding of how to conjugate a French verb, you can use natural language inference to check if the output entails, contradicts, or is neutral to the prompt, and to generate new sentences that entail, contradict, or are neutral to the output.
- Use semantic similarity. Semantic similarity is the measure of how similar two sentences are in terms of meaning. You can use semantic similarity to compare and rank the outputs, and to generate new outputs that are more or less similar to the prompt or the existing outputs. For example, if you want to develop a scientific A/B test to determine the market’s favorite cola, you can use semantic similarity to compare and rank the outputs based on how similar they are to the prompt, and to generate new outputs that are more or less similar to the existing outputs.
- Use paraphrasing and rephrasing. Paraphrasing and rephrasing are the techniques of expressing the same meaning using different words or structures. You can use paraphrasing and rephrasing to simplify or clarify the prompt or the output, and to generate new outputs that are equivalent or alternative to the existing ones. For example, if you want to craft a professional academic abstract summarizing this entire collection of documents, you can use paraphrasing and rephrasing to simplify or clarify the prompt or the output, and to generate new outputs that are equivalent or alternative to the existing ones.
- Use question answering. Question answering is the ability to answer questions based on a given text or context. You can use question answering to extract or verify information from the output, and to generate new outputs based on the answers. For example, if you want to build a schedule of learning to master this collection of documents over the course of an 8 week class that meets Monday and Wednesday from 9:30-10:45 AM starting January 16 and ending March 25, you can use question answering to extract or verify information from the output, such as the topics, the activities, or the deadlines, and to generate new outputs based on the answers, such as reminders, summaries, or feedback.
- Use text summarization. Text summarization is the ability to generate a shorter version of a text that preserves the main points and information. You can use text summarization to condense or highlight the prompt or the output, and to generate new outputs that are more concise or comprehensive. For example, if you want to create a clear understanding of how to conjugate a French verb, you can use text summarization to condense or highlight the prompt or the output, and to generate new outputs that are more concise or comprehensive, such as bullet points, tables, or diagrams.
- Use text generation. Text generation is the ability to generate new text based on a given text or context. You can use text generation to expand or elaborate on the prompt or the output, and to generate new outputs that are more detailed or creative. For example, if you want to develop a scientific A/B test to determine the market’s favorite cola, you can use text generation to expand or elaborate on the prompt or the output, and to generate new outputs that are more detailed or creative, such as graphs, charts, or reports.
- Use text classification. Text classification is the ability to assign a label or a category to a text based on its content or purpose. You can use text classification to organize or filter the outputs, and to generate new outputs based on the labels or categories. For example, if you want to craft a professional academic abstract summarizing this entire collection of documents, you can use text classification to organize or filter the outputs based on their labels or categories, such as background, purpose, methods, results, or conclusions, and to generate new outputs based on the labels or categories, such as headings, subheadings, or keywords.
- Use text sentiment analysis. Text sentiment analysis is the ability to detect the emotion or attitude of a text, such as positive, negative, or neutral. You can use text sentiment analysis to assess or modify the tone or mood of the output, and to generate new outputs based on the emotion or attitude. For example, if you want to build a schedule of learning to master this collection of documents over the course of an 8 week class that meets Monday and Wednesday from 9:30-10:45 AM starting January 16 and ending March 25, you can use text sentiment analysis to assess or modify the tone or mood of the output, such as motivational, encouraging, or challenging, and to generate new outputs based on the emotion or attitude, such as compliments, suggestions, or warnings.
- Use text style transfer. Text style transfer is the ability to change the style of a text, such as formal, informal, humorous, or poetic, while preserving the content or meaning. You can use text style transfer to adapt or transform the output, and to generate new outputs that are more suitable or appealing for the purpose or audience. For example, if you want to create a clear understanding of how to conjugate a French verb, you can use text style transfer to adapt or transform the output, and to generate new outputs that are more suitable or appealing for the purpose or audience, such as a song, a poem, or a story.
- Use text rewriting. Text rewriting is the ability to modify or improve a text, such as correcting errors, removing redundancies, or adding information. You can use text rewriting to revise or enhance the output, and to generate new outputs that are more accurate or informative. For example, if you want to develop a scientific A/B test to determine the market’s favorite cola, you can use text rewriting to revise or enhance the output, and to generate new outputs that are more accurate or informative, such as adding citations, explanations, or recommendations.
- Use text evaluation. Text evaluation is the ability to measure the quality or performance of a text, such as readability, coherence, relevance, or originality. You can use text evaluation to rate or rank the outputs, and to generate new outputs based on the scores or rankings. For example, if you want to craft a professional academic abstract summarizing this entire collection of documents, you can use text evaluation to rate or rank the outputs based on their readability, coherence, relevance, or originality, and to generate new outputs based on the scores or rankings, such as feedback, comments, or suggestions.So there you have it. the top 21 tips for a novice AI learner to be an effective prompt engineer!Here is a link to all of the sources, which you will find are a great basis from which to develop a more comprehensive understanding of AI, its uses and applications and more advanced ways to leverage AI for effective, efficient work creation, work flow and productivity. Thanks for reading.Sources:
Source: https://www.techtarget.com/searchenterpriseai/tip/Prompt-engineering-tips-and-best-practices
Source: https://www.hostinger.com/tutorials/ai-prompt-engineering
Source: https://www.analyticsvidhya.com/blog/2023/05/what-is-prompt-engineering-guide/
Source: https://www.datacamp.com/blog/what-is-prompt-engineering-the-future-of-ai-communication
Source: https://medium.com/@promptora/a-comprehensive-guide-to-prompt-engineering-c16e2e21f395
Source: https://help.openai.com/en/articles/6654000-best-practices-for-prompt-engineering-with-openai-api
Source: https://www.linkedin.com/pulse/designing-effective-prompts-guide-prompt-engineering-trackmind/
Source: https://www.promptingguide.ai/applications/pal
Source: https://mitsloanedtech.mit.edu/ai/basics/effective-prompts/
Source: https://www.ibm.com/topics/prompt-engineering
Source: https://colinscotland.com/unleash-the-power-of-chatgpt-11-epic-prompt-engineering-tips/
Source: https://nextgeninvent.com/blogs/prompt-engineering-best-practices/
Source: https://serpstat.com/blog/ultimate-guide-to-effective-prompt-engineering/
Source: https://www.splunk.com/en_us/blog/learn/prompt-engineering.html
Source: https://www.aiprm.com/resources/guides/prompt-engineering-guide/
Source: https://www.linkedin.com/pulse/power-prompt-engineering-how-create-effective-prompts-bretzfield-1c/
Source: https://www.datacamp.com/tutorial/a-beginners-guide-to-chatgpt-prompt-engineering
Source: https://www.eweek.com/artificial-intelligence/guide-to-prompt-engineering/
*This work was created together with Bing Chat on the Chat GPT-4 backbone.