What is prompt engineering?
Prompt engineering is a concept in artificial intelligence, particularly natural language processing (NLP). It involves discovering inputs that yield desirable or useful results. It works by converting one or more tasks to a prompt-based dataset and training a language model with what has been called “prompt-based learning” or just “prompt learning”.
The GPT-2 and GPT-3 language models were important steps in prompt engineering. Prompt engineering best practices involve exploring the capacity of prompts to generate desirable or useful results and tailoring of the prompt to the task at hand. Prompt engineering can include multitasking, few-shot learning and zero-shot learning.
It has been used to create text-to-image prompts using models like DALL-E, Stable Diffusion and Midjourney. Projects like PromptSource and PromptIDE provide infrastructure to systematize and crowdsource best practices for prompt engineering. PromptChainer is a tool to design multi-step LLM applications that tie together prompting steps, external API calls and user inputs.
Why is prompt engineering important?
1. It helps to improve the accuracy and performance of AI systems
Prompt engineering helps improve the accuracy and performance of AI systems in several ways. First, it allows AI systems to understand words, images, and concepts more accurately by providing the AI with the right words or prompts. Second, prompt engineering can also help increase the size of the AI’s prompt, which can lead to better results with a shorter context in the prompt. Additionally, prompt engineering can improve the accuracy of model completions by fine-tuning the model and by providing content filters to remove potential biases from the AI’s training data. Lastly, prompt engineering can also incorporate more sophisticated structural information into the AI’s models, such as syntactic trees and document structures, which can lead to more accurate and reliable results.
2. It helps to make AI systems more user-friendly
Prompt engineering helps make AI systems more user-friendly by allowing users to interact with the AI using plain language. This makes it easier for users to understand the AI’s responses and to provide the prompt that best fits their desired outcome. By optimizing the prompt, the AI can be trained to better understand the user’s requests and thus provide more accurate and meaningful results. Furthermore, prompt engineering can also help reduce latency and improve performance by optimizing the prompt size. Additionally, prompt engineering can help ensure the responsible use of AI by filtering out potentially biased content in the training data. Ultimately, prompt engineering makes AI more accessible and user-friendly, enabling more people to benefit from its use.
3. It allows AI systems to be more tailored to specific use-cases
Prompt engineering helps AI systems to be tailored to specific use cases by providing the AI with a specific training prompt which will help it to better understand what is expected from it. The prompt should be written in a way that the AI understands, so that it can provide results that are most suitable for the task. When the prompt is designed correctly, the AI can better focus on the task at hand, and produce the desired results. Prompt engineering also helps to ensure that the AI is focusing on the right aspects of the task it is being asked to perform and that it is not wasting time or energy on unnecessary tasks.
4. It allows people to better understand how AI systems work
Prompt engineering is an essential tool for people to understand how AI systems work. Prompt engineering is a way of providing AI with the context and instructions it needs to understand a given task. By providing AI with the necessary information, prompt engineers are able to help AI systems make decisions, respond to queries, and even create products. It is a crucial step in understanding how AI systems work, because without proper instructions AI systems cannot effectively complete tasks. Prompt engineering helps people to understand how AI systems work by providing AI with the instructions it needs to understand a given task and make decisions.
5. It helps to streamline the training and deployment process
Prompt engineering helps to streamline the training and deployment process by providing new interfaces for application development and allowing users to experiment with prompt variations and track prompt performance. Through tools such as PromptSource, PromptIDE, and PromptChainer, users can design data-linked prompts, manage prompts, and iteratively optimize prompts to get the desired results. Additionally, prompt engineering allows users to design multi-step LLM applications that can tie together prompting steps, external API calls, and user inputs, providing a virtually seamless user experience. By systematizing the search process for optimal prompts, prompt engineering can provide an AutoML-style framework, enabling users to spend less time to achieve better LLM results.
6. It improves the overall quality of the AI system
Prompt engineering is a key factor in improving the overall quality of an AI system. By providing an AI system with an effective prompt, it can better understand the inputs and outputs of a task, as well as the user’s intentions. This understanding allows the AI to produce better results, as it is able to interpret the inputs and take appropriate actions to produce desired results. For example, a prompt engineer may provide an AI system with a prompt that describes a particular task or set of goals, such as producing a recommendation for a certain type of product or service. The AI system can then produce a more accurate and tailored outcome. Furthermore, a well-crafted prompt can inform the AI system of the user’s desired outcomes, allowing it to generate more accurate results that are tailored for the user.
7. It helps to improve communication between people and AI systems
Prompt engineering helps improve communication between people and AI systems by allowing humans to craft better prompts that are tailored to the AI’s capabilities. This can result in more accurate responses from the AI, allowing for more effective communication between humans and AI systems. By understanding the nuances of how to phrase the prompt correctly, humans can get the best results from AI and make the most of its potential.
8. It makes AI systems easier to test and verify
Prompt engineering is an important part of making AI systems easier to test and verify. Prompt engineering involves creating clear and concise instructions to the AI system on what it should do and how it should do it. By providing the AI system with a well-defined list of instructions, it can be tested more easily and with more accuracy, allowing engineers to make sure the system is functioning correctly and that it is producing the desired results. Additionally, prompt engineering helps to reduce the amount of time it takes to train and fine-tune the AI system, thereby making it easier to deploy. Ultimately, prompt engineering helps to ensure that AI systems are performing correctly and efficiently and that they are producing the desired results.
9. It helps to increase the speed of the AI system
Prompt engineering can help to increase the speed of an AI system by providing the AI with a smaller set of data to process. By creating a prompt with a specific goal in mind, the model can focus its resources on the most important aspects of the task at hand. This reduces the amount of time and energy needed to complete a task, allowing the AI to work more quickly and efficiently. In addition, fine-tuning the prompt can help improve accuracy while maintaining performance levels, making it possible to have good results with shorter context in the prompt. Ultimately, prompt engineering can be used to optimize the AI system to maximize speed and accuracy.
10. It helps to reduce the amount of human input needed for AI systems
Prompt engineering helps to reduce the amount of human input needed for AI systems by leveraging the large models’ potential to be better prompt engineers. Prompt engineering is the task of finding the best results from the best AI that gets the best from prompt engineering. By encoding a ‘style’ into a prompt library, creatives can generate multiple versions of the same product with just the click of a button. Additionally, thanks to the tight feedback loop between the prompt and the results, creative professionals can generate new, unexpected inspirations that they would not have thought of otherwise. Furthermore, clients can use AI to show creatives exactly what they want, eliminating the time wasted in second-guessing. By using generative AI tools, creatives can reduce the amount of tedious work they have to do, allowing them to focus their efforts on more meaningful tasks.
Different types of prompts and their uses
1. Input prompts
What are some examples of input prompts? Input prompts for AI models can be broken down into two main categories: training prompts and prompts for application development. Training prompts are the sequences of text given to the model for it to learn from, while application development prompts allow for the model to generate a contextual output. Examples of training prompts include input sequences that teach the model how to classify images and captions, while examples of application development prompts include using the model to generate code and evaluate user input.
2. Model parameters
A model parameter is a value that the model may alter independently as it learns. It is used to control the behavior of the model, such as its output in response to certain inputs. For example, the temperature is a commonly used parameter to influence the output of a model. A higher temperature produces more random (and usually creative) outputs, while a temperature of 0 is best for factual use cases such as data extraction and truthful Q&A. Other parameters such as maximum tokens, stop sequences, and frequency help control the model’s behavior as well.
3. Target output
The target output for a classification prompt with an explanation is a statement of the classification with an explanation of why that classification is being made. For example, if a prompt asked to classify a type of fruit, the target output might be “The fruit is an apple. This is because it has a crisp texture and is typically red or green in color.”
4. Modeling algorithms
A modeling algorithm is a set of mathematical equations used to simulate a real-world system or process. It is used to analyze and understand complex systems, and to develop better solutions and strategies. Modeling algorithms are also often used in artificial intelligence and machine learning applications, such as in natural language processing, computer vision, and robotics. They are used to create models that can optimize and automate decision-making processes. Modeling algorithms can be used to create predictive models, which use data to predict outcomes and identify trends, as well as classification models, which define the boundaries between different classes and identify the class of an item.
5. Datasets
Datasets can be used for a variety of tasks, from machine learning to research projects. Examples of datasets include DiffusionDB, which is a database of publicly available materials for use in statistical computing; PartiPrompts, which is a collection of writing prompts for creative writing; Real Toxicity Prompts, which provides data-driven prompts for detecting toxic behavior online; and the Stable Diffusion Dataset, which is a collection of datasets related to diffusion processes. Other datasets include the Anthropic’s Red Team dataset, which contains data collected from red team exercises such as phishing and malware campaigns; WritingPrompts, which provides a collection of writing prompts for a variety of topics; Midjourney Prompts, which contains prompts for creative writing; Awesome ChatGPT Prompts, which provides a collection of chatbot prompts generated by chatbot artificial intelligence; and P3 – Public Pool of Prompts, which is a publicly available database of curated prompts for creative writing. Each dataset has its own specific use, such as for statistical computing, research, creative writing, and detecting toxic behavior.
6. User interfaces
What are some examples of user interfaces and what are their uses? Prompt engineering is an increasingly important user interface for AI, enabling users to craft prompts to get the output they want. For example, text-to-text or text-to-image models can be used to craft prompts with adjectives or phrases to generate images of a particular object. In terms of other user interfaces, Bach and Sanh et al. built PromptSource, an integrated development environment to systematize and crowdsource best practices for prompt engineering. Strobelt et al. developed PromptIDE, a handy visual platform to experiment with prompt variations, track prompt performance, and iteratively optimize prompts. Wu et al. formalize the notion of an LLM chain and propose PromptChainer as a tool to design multi-step LLM applications. In addition, Copilot makes it easy for users to reject Codex’s suggestions, and VS Code is an IDE that allows users to edit Codex’s output if needed. Lastly, OpenAI and Azure OpenAI Service offer content filtering capabilities to responsibly use OpenAI models in production.
7. OpenAI
OpenAI is an artificial intelligence research team that focuses on developing machine learning algorithms and models with the aim of advancing the field of AI. OpenAI’s most notable project is the GPT-3 language model, which is the largest natural language model ever created and offers an unprecedented level of accuracy in text generation. OpenAI’s models are used for a variety of tasks, from natural language processing (NLP) to question answering and automated customer service. The GPT-3 model is capable of generating text from scratch, understanding concepts, performing tasks, and providing answers to questions. The model can be used to generate job descriptions, create presentations, design AI solutions, and many other use cases. Beyond text generation, OpenAI’s models can be used to identify objects in images, generate music, define strategies for gaming and robotics, and much more.
8. Content filters
Content filters are tools used to block access to certain types of content, such as websites, apps, and search results. They can be used to protect users from accessing inappropriate content, to limit distractions, or to enforce certain standards of Internet usage in public places like libraries or schools. Content filters typically rely on keyword-based filtering or blacklisting to identify and block access to restricted content, though they can also be configured to only allow access to specific websites or categories. Content filters can also be used to enforce security and data privacy by blocking access to malicious websites or content that could be used to compromise sensitive data.
9. Bot Commands
Task Description: Generate an answer that defines and explains bot commands
Context: Bot commands are commands used by automated chatbots to trigger a response from a user.
Examples: A bot command might be “!hello” which will trigger the chatbot to send a message saying “Hello, how may I help you?”
User Input: A user might type “What are bot commands and what are their uses?”.
10. Prompts Sources
Prompts are an important part of programming AI models like DALL·E, Midjourney & GPT-3. There are a number of sources available for finding quality prompts that produce the best results and save money on API costs. These include PartiPrompts, Real Toxicity Prompts, DiffusionDB, P3 – Public Pool of Prompts, WritingPrompts, Midjourney Prompts, Awesome ChatGPT Prompts, Stable Diffusion Dataset, and Anthropic’s Red Team dataset.
In order to make prompt engineering and application development easier, tools like PromptSource, PromptIDE and PromptChainer have been made available. PromptSource is an integrated development environment to systematize and crowdsource best practices for prompt engineering, while PromptIDE is a visual platform to experiment with prompt variations, track prompt performance, and iteratively optimize prompts. PromptChainer is a tool to design multi-step LLM applications by tying together prompting steps, external API calls, and user inputs.
PromptBase is an early marketplace for DALL·E, Midjourney, Stable Diffusion & GPT-3 prompts, where people can buy and sell quality prompts. Writing good prompts is a matter of understanding what the model “knows” about the world and then applying that information accordingly, almost like the game of charades. By providing just enough information in the training prompt, the model can work out the patterns and accomplish the task at hand.
How to create prompts for end-to-end testing?
Step 1: Identify the task you want to accomplish
Prompt: How do you identify the task you want to accomplish for end-to-end testing? Let’s think about this step by step and explain it in simple terms.
Step 2: Describe the task and general setting
When designing a prompt for end-to-end testing, it is important to provide the model with enough context to understand the task and setting. Start by describing the task in natural language, including any additional components of the task description. Provide the model with high-level contexts such as API hints and database schema to help it understand the task. Then, give the model examples of what you want it to generate. Finally, remind the model of what the user has said before. To ensure that the model can provide the expected results, it is important that you provide it with a complete prompt with all of the necessary components. Step-by-step, the process looks like this:
1. Describe the task in natural language.
2. Provide high-level context (API hints, database schema).
3. Give examples of what you want the model to generate.
4. Remind the model of what the user has said before.
5. Provide the model with a complete prompt that includes all of the necessary components.
By providing the model with all of these components, you can ensure that your model is able to provide the expected results for end-to-end testing.
Step 3: Articulate the desired output format through examples (example 1, example 2)
In order to ensure that our model is generating the desired output and to perform end-to-end testing, it is important to provide examples of the type of output we are expecting. We can do this by adding examples to a prompt which demonstrate to the model the type of output we are targeting. For example, if we have a movie review sentiment classifier, a few examples we could give are:
Example 1:
Input: “I really enjoyed this movie!”
Output: The sentiment of this review is positive.
Example 2:
Input: “I don’t know, it was ok I guess…”
Output: This review is neutral.
Example 3:
Input: “What a waste of time, would not recommend this movie.”
Output: This review is negative.
In order to make sure that the model is reliably producing the desired output, it is important to provide the model with appropriate examples with clear instructions and expected output. In the case of few-shot learning, the prompt should include both the example input and the expected output, and one should test the output for uncertainty by using the likelihood endpoint. Additionally, one should experiment with different styles of writing, such as writing in the style of a news article, a blog post, or a dialogue, to ensure that the model is understanding the desired output.
Step 4: Use leading words to nudge the model toward a particular pattern
How can leading words be used to nudge a model toward a particular pattern during end-to-end testing? Let’s think about this step by step. First, consider the task at hand and the context in which it is set. If a task involves summarizing content, for example, you may want to provide a prompt such as “To summarize in plain language,” or “The main point to take from this article is that.” Additionally, you may need to provide the model with API hints and database schemas to help the model understand the task better. Then, consider examples of what you want the model to generate. You can provide the model with examples from your dataset, or use the likelihood feature in the GPT-3 playground to see if there are particular words, phrases, or structures that the model has trouble understanding. Finally, remind the model what the user has said before. This helps the model understand the context and generate more coherent results.
Step 5: Try multiple formulations of your prompt to get the best generations
When generating with our models, it can be beneficial to try a variety of different prompts to get the best generations for end-to-end testing. Here are a few steps to follow in order to achieve this:
1. Start by writing a prompt that outlines the task you are trying to solve. This should provide basic instructions to the model about what type of generation you are after.
2. Include examples in your prompt. Examples should demonstrate the type of output the model should be producing. This will help the model better understand the task and what type of generations are desired.
3. Try different formulations of the same prompt. Even though the different prompts may sound similar to humans, they can lead to different generations as the model has learned that the different formulations are typically used in different contexts and for different purposes.
4. Use our likelihood feature in the playground to see if there are any words, phrases, or structures that the model is struggling to understand.
5. Experiment with different temperatures in order to get the best generations.
6. Try rewriting the prompt as prose instead of commands. This way of writing will be less likely to be misunderstood by the model.
Step 6: Reduce “fluffy” and imprecise descriptions
To reduce “fluffy” and imprecise descriptions for end-to-end testing, be as specific as possible in your prompts. Include the topic, length, style, structure, and intent. When using generate, try a range of different prompts for the problem you are trying to solve. Additionally, you can use the likelihood feature to see if there are any words, phrases, or structures that the model is struggling with. Finally, add phrases like “Let’s think about this step by step” or “in simple terms” to help make the response easier to understand.
Step 7: Debug and evaluate prompts as they are generated
To debug and evaluate prompts as they are generated for end-to-end testing, it is first important to understand what the model “knows” about the world. By providing enough information via the training prompt, the model can work out the patterns and accomplish the task at hand, much like in the game of charades. Once this is established, it is necessary to think about what type of phrases should be included in the prompt. Examples of such phrases could include “Let’s think about this step by step” or “in simple terms,” “easy to understand,” or “like I’m five.”
It is also important to consider prompt formats that align well with the task at hand. A prompt should consist of a high-level task description, high-level context, examples of what the desired output should be and the user input. To ensure the model is receiving the most effective input and to help debug and evaluate the prompts, tools such as PromptSource or PromptIDE can be utilized. PromptSource allows for templating language to define data-linked prompts and general tools for prompt management, whereas PromptIDE allows for prompt variations, tracking of prompt performance, and optimization of prompts.
By using these tools and evaluating the prompts, it is possible to debug and evaluate them as they are generated for end-to-end testing.
How does AI end-to-end testing work?
Step 1: Start by identifying the goals and objectives of the end-to-end testing. This helps to ensure that you are testing the correct components, so that your results are accurate.
Step 2: Set up the environment for the testing. This involves setting up the systems, data, and networks needed to run the tests.
Step 3: Develop the AI end-to-end tests. Different types of tests can be used, such as unit tests, integration tests, acceptance tests, and stress tests.
Step 4: Execute the tests and analyze the results. This step involves running the tests and evaluating their results. This includes reviewing the test cases and identifying any errors or issues.
Step 5: Refine and re-run the tests as needed. This step involves making necessary changes to the tests and re-running them until desired results are achieved.
Step 6: Document the tests and their results. This step involves documenting the tests, their results, and any changes that were made. This documentation should be shared with the development team to ensure that any future changes are made in accordance with the tests.
What is an example of an input for AI end-to-end testing?
What is an example of an input for AI end-to-end testing? An example of an input for AI end-to-end testing could be to provide the AI with a set of test cases that cover the different features of the application, along with expected outcomes for each test case. This allows the AI model to be tested for accuracy, performance, and scalability.
What is the difference between data and text data?
Data is a collection of facts, figures, and information about a particular subject or topic, while text data is a collection of words, phrases, and sentences that contain information about a particular subject or topic. Data can be structured, organized, or unstructured, while text data is structured and organized in sentences, paragraphs, and other written forms. Data can be stored in different formats, such as numerical data, graphical data, or tabular data, while text data is typically stored in text files or other documents. Data can be used to analyze and track trends, while text data can be used to provide detailed information about a subject or topic.
What parameters do you need to consider when building a model for AI end-to-end testing?
When building a model for AI end-to-end testing, there are several parameters to take into account. These include performance, interaction design, responsible use, model, temperature, and other hyperparameters. Performance is important for ensuring prompt latencies and accuracy, so engineers should fine-tune the model for best results. Interaction design helps guide users in their interaction with Codex, so it’s important to provide clear instructions and offer users the ability to reject Codex’s suggestions. Responsible use is also important, as models can reflect the biases of the training data they were fed. Therefore, a content filter should be implemented to ensure the accuracy of the model. Finally, model and temperature can be adjusted to determine the model’s output. By adjusting these parameters, engineers can tune the behavior of OpenAI models for the best results.
What is the difference between completion and model completion?
Completion and model completion are both techniques used to process input text and generate output. Completion is a task-agnostic approach that uses general-purpose language models to generate responses. This approach is useful when the desired output is unknown and you need to generate a wide array of potential responses. Model completion is a task-specific approach that fine-tunes a model to generate tailored responses for the desired output. This approach is useful when the desired output is known and can be specified in a prompt, as the model will generate more accurate and relevant responses. Both completion and model completion can improve the user experience in applications, though model completion has the potential to be more accurate and efficient.
How does OpenAI make use of AI end-to-end testing?
OpenAI has taken steps to ensure the quality and reliability of their AI solutions by introducing AI end-to-end testing. This method of testing makes sure that each component of an AI system is tested individually and then again as a whole. Here’s a step-by-step guide on how OpenAI makes use of AI end-to-end testing:
1. First, OpenAI will build a testing environment to simulate how their AI solution will be used in the real world. This includes setting up the input and output parameters, along with the data sets and any other variables needed.
2. Next, OpenAI will create a set of test cases for their AI solution. This involves defining various scenarios that the AI must pass in order to be certified as reliable.
3. After that, the AI is tested thoroughly against the test cases. This includes a range of scenarios and conditions that the AI must pass in order to be certified as reliable.
4. Finally, the results of the tests are analyzed carefully. OpenAI will then make the necessary changes and adjustments to the AI to ensure that it works as expected.
By following this process, OpenAI is able to ensure the quality and reliability of their AI solutions. End-to-end testing is an important part of the development process and is essential for OpenAI to make sure their AI solutions are able to perform as expected.
What are the most important tasks for AI end-to-end testing?
The most important tasks for AI end-to-end testing include performance testing, interaction design, and responsible use.
1. Performance Testing: Test the latency and accuracy of the AI model’s completions by increasing the prompt size to see how the model responds. Fine-tune the model to improve accuracy and performance by decreasing the prompt size.
2. Interaction Design: Guide users on how to interact with the AI model for best results. Make it easier for users to reject the suggestions of the model if it produces unreliable results.
3. Responsible Use: Use content filters from OpenAI or Azure OpenAI Service to ensure that biases from the training data are not reflected in the model.
Overall, the use of AI end-to-end testing is important in order to ensure that the model is working as expected and that it is presenting reliable results to the users.
What are the benefits of using AI end-to-end testing?
The benefits of using AI end-to-end testing are numerous. AI end-to-end testing can increase accuracy and reduce the time it takes to complete tests. AI is also able to detect errors more quickly than manual testing and can identify patterns or trends in the data. AI can also provide a more detailed analysis of the data, allowing for more targeted testing and a better understanding of the system or application. AI can also provide better coverage for testing, helping to ensure that all areas of the system or application are properly tested. In addition, AI-driven tests can be automated, which can save time and money. Finally, AI end-to-end testing can provide insights that can be used to improve the system or application, making it more efficient and effective.
What tools are used to develop AI end-to-end testing?
AI end-to-end testing can be developed using a variety of tools, including:
* Prompt Engineering: Prompt engineering is the process of translating the results of AI algorithms into words that the AI can understand. Prompt engineering tools such as Visual Prompt Builder can be used to create simple text prompts which can be used to generate impressive results from the AI.
* Text-To-Text: AI language models such as GPT-3, BERT, and others are used to generate text-to-text output.
* Text-To-Image: AI image and text generation tools such as OpenAI’s DALL-E 2 and GPT-3 can be used to generate text-to-image output.
* Text-To-Video: AI video generation tools such as Meta’s Make-A-Video and Google’s Imagen Video can be used to generate text-to-video output.
* Text-To-3D: AI diffusion models such as DreamFusion, developed by Google Research, can be used to generate text-to-3D output.
