OpenAI Prompt Engineering: GPT- 4 Everything You Need to Know

An AI revolution is happening with Chat Gpt. With the aid of natural language processing, ChatGPT, an artificial intelligence (AI) chatbot, can simulate human speech. In response to queries from users, the language model can generate a variety of textual content, such as emails, articles, essays, social media postings, and code. CPT-3 and CPT-4 are both advanced language models developed by OpenAI, but there are key differences between them. CPT-3, known for its breakthrough performance in understanding and generating human-like text, set a new standard in natural language processing. However, CPT-4, the successor to CPT-3, takes this a step further. 

It’s more advanced, with improvements in understanding context, generating more accurate and relevant responses, and better handling of nuanced language. CPT-4 also demonstrates a greater ability to learn from smaller amounts of data and to provide more detailed and specific answers. These enhancements make CPT-4 a more powerful and refined tool compared to CPT-3, offering a closer emulation of human-like understanding and interaction. GPT-4 is the most advanced version with a vast array of features, which is a recent update from GPT-3. A manual on prompt engineering was just released by OpenAI, the company behind ChatGpt. The user can achieve the best answer from ChatGpt by following the six tactics in the guide, which include examples.

The Six Strategies to Get Better Response from ChatGpt

Prompt Engineering Strategy 1: Write clear instructions

Users must explain their ideas in detail using as many aspects as possible to improve the idea’s description because it is a machine. Users are required to decide on ChatGpt-4’s response. i.e., request brief responses if the results are too lengthy. Request writing at the expert level if the outputs are too basic. Give an example of the format you would prefer to see if you don’t like this one.

Tactic 1: Include details in your query to get more relevant answers

This is used to revolve around the principle of providing more specific and detailed information in your question to receive more accurate and relevant responses from the model.

Example: For a vague question like, “How to cook food?” the model might give a very general response because it’s unclear what type of food you’re referring to, what cooking method you’re interested in, or your level of cooking experience. The answer could range from basic cooking techniques to various recipes, which might not be what you’re looking for.

However, asking a more detailed question like, “How to bake a chocolate cake using a microwave for someone who’s a beginner in baking?” gives the model much more specific information to work with. As a result, it can provide a more targeted response, perhaps including a simple microwave chocolate cake recipe suitable for beginners, along with tips for microwave baking.

Tactic 2: Ask the model to adopt a persona

The tactic of asking the model to adopt a persona involves instructing the AI to respond as if it were a specific character or individual with a distinct role, background, or perspective. This helps in tailoring the response to fit a certain style or viewpoint, making the interaction more engaging and relevant to the user’s needs.

Example: Let’s say you’re interested in historical events and you ask the model, “Pretend you are a historian specializing in ancient Rome. Can you describe the social structure of Roman society?” By asking the model to adopt the persona of a Roman historian, the response will be crafted as if an expert in Roman history is answering. This approach encourages the model to provide a more detailed, accurate, and contextually appropriate response, incorporating the perspective and expertise one would expect from a Roman historian.

Tactic 3: Use delimiters to clearly indicate distinct parts of the input

The tactic of using delimiters to indicate distinct parts of the input involves structuring your query in a way that separates different elements or questions. This helps the AI model to understand and address each part of your query more effectively.

Example: Suppose you have two separate questions for the model: one about a scientific concept and another about a historical event. You might structure your query like this: “Question 1: [What causes the seasons to change on Earth?]”  “Question 2: [What were the main causes of World War I?]”

In this example, “Question 1” and “Question 2” serve as labels, while the square brackets [] act as delimiters, clearly separating the two different inquiries. This format helps the model recognize that these are two distinct questions, allowing it to address each one individually and more accurately. Without such clear delimiters, the model might confuse the two questions or blend the answers, leading to less coherent or relevant responses.

Tactic 4: Specify the steps required to complete a task

The tactic of specifying the steps required to complete a task involves breaking down your request into a clear, step-by-step format. This approach guides the AI to address each part of the task in a structured and sequential manner, leading to more accurate and comprehensive responses.

Example: Imagine you’re trying to learn how to bake a cake. Instead of simply asking, “How do I bake a cake?”, you can make your query more effective by specifying the steps involved. Your question might look like this: “List the steps to bake a chocolate cake, including: 1. Preparing the ingredients 2. Mixing the batter 3. Preheating the oven 4. Baking the cake 5. Cooling and decorating”

In this example, each numbered item represents a specific step in the cake-baking process. By laying out your request in this detailed, step-by-step manner, you help the AI understand and address each part of the task separately. This makes it easier for the model to provide a thorough and practical guide to baking a cake, covering everything from preparation to decoration.

Tactic 5: Provide examples

The tactic of providing examples in your query is a method to guide the AI model by giving it specific references or scenarios to base its response on. This helps the model understand the context or style of the response you’re looking for, leading to more accurate and relevant answers.

Example: Let’s say you want to create a fantasy story and need help with character names. Instead of asking vaguely, “Give me fantasy character names,” you can make the query more effective by providing examples: “Provide fantasy character names similar to those in J.R.R. Tolkien’s ‘Lord of the Rings’, like Aragorn, Legolas, and Gandalf.”

In this example, mentioning specific names from ‘Lord of the Rings’ serves as a clear reference for the kind of names you’re interested in. This context enables the AI to generate names that align with the style and feel of Tolkien’s work, ensuring that the suggestions match your creative vision more closely.

Tactic 6: Specify the desired length of the output

The tactic of specifying the desired length of the output is a strategy where you indicate how long or detailed you want the AI model’s response to be. This helps the model tailor its reply to fit your specific needs, whether you’re looking for a brief overview or an in-depth explanation.

Example: Suppose you are seeking a summary of a complex topic, like climate change. Instead of asking a general question, you can specify the length to get a response that fits your requirement: “Can you give me a summary, in about three sentences, of the main causes of climate change?”

In this example, by requesting a summary “in about three sentences,” you’re guiding the AI to condense its response into a concise format. This ensures that the model provides a succinct overview rather than an extensive discussion, which is particularly useful when you need quick insights or are limited by time or space constraints.

Prompt Engineering Strategy 2: Provide Reference Text

Language models aren’t perfect and can sometimes give wrong answers, especially for less common topics or when specific references are needed. However, if you give these models good information to start with, they’re more likely to provide accurate and trustworthy responses.

i.e., The language model, ChatGPT-4, there’s a possibility it might give answers that aren’t true or accurate. This isn’t because the model wants to deceive, but because it generates responses based on a vast amount of information it has been trained on, and sometimes it can make mistakes or create information that seems correct but isn’t.

The risk of getting an untrue response is higher with topics that are not well-known (obscure) or highly specific. Also, when the model is asked to provide citations or URLs (links to websites), it may struggle because it can’t browse the internet or recall specific URLs from its training data.

If the user provides the language model with specific reference material or clear context, it can use that information to give more accurate responses. And this is the solution to the problem of potential inaccuracies. According to the manual, techniques such as instructing the model to answer using a reference text and instructing the model to answer with citations from a reference text can help overcome this problem.

Tactic 1: Instruct the model to answer using a reference text

The tactic of instructing the model to answer using a reference text involves asking the AI to base its response on a specific piece of text or document. This approach helps ensure that the model’s answer is aligned with the information, style, or context of the reference material, leading to more accurate and relevant responses.

Example: Imagine you’re studying Shakespeare’s “Romeo and Juliet” and you have a question about the play’s themes. You could ask: “Using the text of ‘Romeo and Juliet’ by Shakespeare, explain the theme of fate versus free will in the play.”

In this example, by specifying that the model should use “the text of ‘Romeo and Juliet'”, you’re directing the AI to focus its analysis and response on the actual content of the play. This ensures that the model’s explanation of the theme is grounded in specific examples and interpretations from Shakespeare’s work, rather than general or unrelated observations. This tactic is particularly useful for academic or literary analysis, where referencing the original text is crucial for accuracy and depth of understanding.

Tactic 2: Instruct the model to answer with citations from a reference text

The tactic of instructing the model to answer with citations from a reference text involves asking the AI to not only base its response on a specific document or source but also to explicitly reference or cite parts of that text in its answer. This approach enhances the credibility and accuracy of the response, as it directly ties the model’s answer to the authoritative source.

Example: Let’s say you’re writing a research paper on the impacts of climate change and need detailed information with sources. You could ask: “Can you explain the effects of global warming on polar ice caps, citing specific findings from the IPCC’s latest climate report?”

In this scenario, by asking the model to cite the IPCC’s climate report, you’re directing the AI to pull information directly from a recognized and reliable source. This not only ensures that the information is accurate and relevant but also provides you with the exact source of the information, which is particularly useful for academic research, professional reports, or any context where citing authoritative sources is important. The model’s response will include direct references or quotes from the IPCC report, adding depth and validation to the answer.

Prompt Engineering Strategy 3: Split Complex Tasks into Simpler Subtasks

Complex tasks are more prone to errors, but you can reduce these errors by breaking the complex task into a series of smaller, simpler tasks. Each small task builds upon the previous one, making the entire process more efficient and less prone to mistakes. It’s a way of organizing work that makes big, challenging tasks easier to handle.

Specifically, the user must provide minor features or inquiries to ChatGpt-4 that convey his broader idea. This will help to get the best and most accurate response. To accomplish this, ChatGpt-4 recommends using strategies like intent classification to determine which user queries will result in the most pertinent instructions, summarizing or filtering previous dialogue, and piecemeasuring lengthy documents to create a comprehensive summary recursively for dialogue applications that call for lengthy conversations.

Tactic 1: Use intent classification to identify the most relevant instructions for a user query

The tactic of using intent classification to identify the most relevant instructions for a user query involves understanding the underlying purpose or goal of the user’s question. By determining the user’s intent, the AI can provide more accurate and targeted information that directly addresses the user’s needs.

Example: Suppose a user asks, “What are some effective methods for time management?” The model uses intent classification to discern that the user intends to find practical strategies for managing time more efficiently. Recognizing this, the model then focuses on providing specific time management techniques, such as the Pomodoro Technique, time blocking, or setting SMART goals, rather than giving general advice about productivity.

This approach ensures that the response is tailored to what the user is looking for, providing them with the most relevant and useful instructions based on their specific query.

Tactic 2: For dialogue applications that require very long conversations, summarize or filter previous dialogue

The tactic of summarizing or filtering previous dialogue in long conversation applications is designed to maintain clarity and focus in interactions that involve extensive exchanges of information. This approach involves condensing previous parts of the conversation or highlighting key points, which helps keep the dialogue coherent and on-topic, especially in scenarios where the conversation might span several sessions or cover complex topics.

Example: Imagine a scenario where a user is discussing a detailed project plan with the AI over a series of messages. The conversation covers various aspects like objectives, timelines, resources, and potential challenges. As the discussion becomes lengthy, key details might get buried under the sheer volume of information exchanged. To manage this, the AI can interject with a summary at appropriate intervals, such as, “To recap, we’ve established the project’s main objectives as X and Y, with a completion timeline of Z months. We’ve also identified potential challenges in resource allocation. Shall we now discuss solutions to these challenges?”

This summary filters the extensive dialogue into its most essential elements, making it easier for the user to follow the conversation and for the AI to provide relevant further responses. It’s particularly useful in customer service, therapy, consulting, and other settings where long, detailed conversations are common.

Tactic 3: Summarize long documents piecewise and construct a full summary recursively

The tactic of summarizing long documents piecewise and constructing a full summary recursively is a method where a large, complex document is broken down into smaller sections, each of which is summarized individually. Then, these individual summaries are combined to form a comprehensive overview of the entire document. This approach ensures that all key aspects of the document are captured accurately in the final summary.

Example: Consider a detailed report on renewable energy. The report might include several chapters, each focusing on different aspects such as solar energy, wind energy, policy implications, and case studies. To summarize this report, the AI would first tackle each chapter separately, providing a concise summary of the key points in each. For instance, it would summarize the solar energy chapter by highlighting its main advancements, benefits, and challenges. Once all chapters are summarized individually, the AI then integrates these separate summaries into a cohesive overall summary. This final summary encapsulates the essential points from each chapter, offering a clear and comprehensive overview of the entire report on renewable energy.

This piecewise and recursive summarization tactic is especially useful for handling long and complex documents, ensuring that the essence of the text is captured without oversimplifying or omitting crucial information.

Prompt Engineering Strategy 4: Give the Model Time to “Think”

When ChatGpt-4, tries to answer questions quickly, they often make more mistakes. But if they take time to think through the steps of their answer, like explaining their thinking process, they give better, more accurate answers. Asking the model to show its “chain of thought” helps it to work out the right answer more dependably.

Therefore, to prevent errors, the user can employ the following tactics: question the model if it missed anything on earlier passes, utilize an inner monologue or a series of inquiries to conceal the model’s reasoning process, and direct the model to solve the problem on its own before proceeding.

Tactic 1: Instruct the model to work out its own solution before rushing to a conclusion

The tactic of instructing the model to work out its solution before rushing to a conclusion involves guiding the AI to think through a problem step-by-step, rather than jumping directly to an answer. This approach helps in providing more reasoned, thoughtful, and accurate responses.

Example: Let’s say you ask the model a math problem, such as, “If I buy 5 apples at $1 each and a $2 coupon applies, how much do I pay?” Instead of immediately giving the final answer, the model would first break down the problem: Calculate the total cost without the coupon: 5 apples at $1 each equals $5. Then, apply the $2 coupon to the total cost: $5 – $2. Finally, calculate the discounted total: $3.

By working through the problem in steps, the model not only arrives at the correct conclusion but also demonstrates its reasoning process. This method is especially useful for complex problems where understanding the process is as important as the final answer. It ensures that the model’s responses are well-thought-out and transparent.

Tactic 2: Use inner monologue or a sequence of queries to hide the model’s reasoning process

The tactic of using an inner monologue or a sequence of queries to hide the model’s reasoning process involves having the AI internally reason through the steps of a problem before presenting a final answer. This approach is different from explicitly showing the step-by-step reasoning in the response. Instead, the model does the reasoning ‘behind the scenes’ and only presents the conclusion or the most relevant information to the user.

Example: Suppose you ask the model, “What are the potential impacts of deforestation on the global climate?” Instead of detailing every step of its reasoning process, the model internally considers various aspects like carbon emissions, loss of biodiversity, and changes in weather patterns. After this internal deliberation, it provides a consolidated answer: “Deforestation can significantly impact the global climate by increasing carbon emissions, leading to a rise in global temperatures, and contributing to biodiversity loss and altered rainfall patterns.”

In this example, the model doesn’t explicitly outline how it connected deforestation to each of these impacts. Instead, it processes this information internally and presents a concise, informed response. This tactic makes the response more streamlined and user-friendly, especially when the user is looking for a straightforward answer rather than a detailed explanation of the reasoning process.

Tactic 3: Ask the model if it missed anything on previous passes

The tactic of asking the model if it missed anything on previous passes is a method to ensure comprehensiveness and accuracy in the information provided. It involves prompting the model to review its previous responses or the information given and to check if there are any important details or aspects that it might have overlooked.

Example: Imagine you’re using the model to gather information about the best practices for sustainable gardening. After receiving an initial set of tips and advice, you could then ask, “Did you miss any critical aspects or tips on sustainable gardening that should be included?” This prompt encourages the model to reassess its previous response and add any significant points it might have missed the first time around, such as details about water conservation techniques or specific sustainable gardening tools, ensuring that the response is as complete and useful as possible.

This tactic is particularly valuable in scenarios where thoroughness is critical, and it helps in capturing the full scope of information on a given topic.

Prompt Engineering Strategy 5: Use External Tools

To improve how well the language model works, the user can use the results from other tools to help it. For instance, OpenAI’s Code Interpreter can do calculations and run programming code, which the language model might not do well on its own. If there’s a job that another tool can do better or faster than the language model, it’s a good idea to use that tool. This way, you combine the strengths of both the language model and the other tools.

Tactic 1: Use embeddings-based search to implement efficient knowledge retrieval

The tactic of using embeddings-based search for efficient knowledge retrieval involves using a special type of search technology known as ’embeddings-based search’. Embeddings are a way of converting words or phrases into numerical values in such a way that similar meanings are represented by similar numerical values. By using these embeddings, the search process becomes much more efficient and accurate, especially in finding relevant information from a large database. It’s like having a very advanced search engine that understands the meaning of your query, not just the specific words.

Example: Let’s say you’re researching the effects of climate change on ocean temperatures. An embeddings-based search would involve the AI model processing your query and then finding and retrieving the most relevant academic papers, data, and articles on this topic. The model uses embeddings (numerical representations of text) to understand the context and meaning of your query, allowing it to find the most pertinent and accurate information from a vast database.

Tactics 2: Use code execution to perform more accurate calculations or call external APIs

Use Code Execution to Perform More Accurate Calculations or Call External APIs tactic is about running actual code to perform tasks. For instance, if a task involves complex calculations, the model can execute code to ensure the calculations are accurate. Also, this can include calling external APIs (Application Programming Interfaces). APIs allow the model to interact with external services or data sources, which can greatly enhance its capabilities.

Example: Imagine you’re building a weather forecasting application and need accurate, real-time data. The AI model could use code execution to call an external weather API, fetch current weather data, and perform calculations to predict future weather patterns. This capability allows the model to provide up-to-date and precise weather forecasts by integrating live data and complex computational algorithms.

Tactic 3: Give the model access to specific functions

Giving model access to specific functions involves enhancing the language model by allowing it access to specific functions or capabilities. These functions could be anything from mathematical operations to more complex data-processing tasks. By having access to these functions, the model can perform a wider range of tasks and provide more detailed and accurate responses. It’s like giving the model a set of tools that it can use to solve different problems more effectively.

Example: Suppose you’re using AI to help with financial planning. By giving the model access to specific functions, such as compound interest calculators or investment risk analysis tools, it can provide more nuanced and practical financial advice. For instance, you could ask how to optimize your retirement savings, and the model could use these functions to calculate optimal savings rates or investment strategies based on your age, income, and goals.

Prompt Engineering Strategy 6: Test Changes Systematically

Making something perform better is simpler if the user can track its performance. Sometimes, changing how you ask a question or give an instruction can make things better for a few specific cases, but might not work as well in general. So, to know if a change is improving things, the user should test it in a wide range of different situations. This big set of tests is called a “comprehensive test suite” or an “eval.” It helps to confirm if the change is truly beneficial overall.

Tactic 1: Evaluate model outputs with reference to gold-standard answers

The tactic of evaluating model outputs concerning gold-standard answers means checking the answers from a model and comparing them with the best possible answers known as ‘gold-standard’ answers. This is like grading a test by comparing the answers to a perfect score sheet to see how well the model is doing.

Example: Suppose you’re using AI to learn about the French Revolution. After the model provides an explanation or a summary of the event, this response is then compared against a gold-standard answer, which might be a well-researched, expertly-written summary from a reputable history textbook or an academic paper. The comparison involves assessing how well the AI’s response matches up in terms of accuracy, completeness, and depth of information with the gold standard. This evaluation helps in determining the quality and reliability of the AI’s answer. If the AI’s response closely aligns with the gold-standard answer, it’s considered to be of high quality; if not, it indicates areas where the model might need improvement.

Features of ChatGpt- 4

ChatGPT-4, an advanced AI model from OpenAI, presents a wide array of cutting-edge features, making it a versatile tool in the realm of artificial intelligence. Its text generation capability is notably sophisticated, allowing it to produce coherent, context-aware, and human-like text across various genres and topics. Alongside this, its function-calling feature enables it to execute specific tasks, like performing calculations or coding, enhancing its utility in technical fields.

ChatGPT-4 also utilizes embeddings, which are sophisticated representations of text data, to improve the accuracy and relevance of its responses. The model’s fine-tuning ability means it can be tailored to specific applications or industries, offering more specialized and targeted outputs. A significant breakthrough is its image generation feature, where it can create detailed and contextually relevant images based on textual descriptions. In the realm of vision, ChatGPT-4 can interpret and analyze visual data, adding a layer of visual understanding to its skillset.

Text-to-speech and speech-to-text features enable it to interact in spoken language, broadening its accessibility and applications in voice-activated systems. The moderation feature in ChatGPT-4 is crucial for filtering out inappropriate content, ensuring safe and responsible use. Collectively, these features position ChatGPT-4 as a highly advanced and multifaceted AI model, suitable for a wide range of applications from creative arts to technical problem-solving.

Conclusion

OpenAI’s Prompt Engineering for GPT-4 represents a significant stride in the realm of artificial intelligence and natural language processing. This advanced technology has not only enhanced the way we interact with AI but has also opened up a plethora of opportunities for more accurate, efficient, and nuanced communication. With its sophisticated algorithms, GPT-4 can understand and respond to prompts with an unprecedented level of contextual awareness, making it a powerful tool in various fields such as education, creative writing, and customer service. However, it’s important to navigate the challenges of ethical considerations and potential biases. As we continue to explore and refine these technologies, the possibilities of what can be achieved with AI like GPT-4 are virtually limitless, ushering in a new era of human-AI collaboration.