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Prompts Manager
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By adhering to this structured approach and utilizing real-world examples, you will develop a strong understanding of prompt engineering, enabling you to create effective prompts for ChatGPT.
Continued practice is essential for refining your prompt engineering skills. As you gain experience, you will develop a more intuitive understanding of how to craft the ideal prompt for any situation. Additionally, do not hesitate to consult online resources, such as GitHub repositories, for further inspiration and examples.
In this section, we will dive deeper into the latest prompt engineering techniques for ChatGPT, covering topics such as its applications, limitations, papers, and additional reading materials.
ChatGPT, a new model trained by OpenAI, is designed to interact conversationally. It can follow instructions in a prompt to provide appropriate responses within the context of a dialogue. ChatGPT can assist with answering questions, suggesting recipes, writing lyrics in a certain style, generating code, and much more.
This model is trained using Reinforcement Learning from Human Feedback (RLHF). While it is more capable than previous GPT iterations and trained to reduce harmful and untruthful outputs, it still has limitations. We will cover some of the capabilities and limitations with concrete examples.
Let's review a basic example of a conversation with ChatGPT. In this scenario, we create a conversational system that generates technical and scientific responses to questions. The prompt looks like this:
The following is a conversation with an AI research assistant. The assistant tone is technical and scientific.
Human: Hello, who are you? AI: Greeting! I am an AI research assistant. How can I help you today? Human: Can you tell me about the creation of black holes? AI:
This example demonstrates the two main components: the intent or explanation of what the chatbot is and the identity, which instructs the style or tone the chatbot will use to respond.
With ChatGPT, developers can create multi-turn conversations using the chat format as input. The model expects a series of messages and generates a response based on the conversation's context.
For example, a chat using the ChatGPT API would look like this:
import openai
openai.ChatCompletion.create(
model="gpt-3.5-turbo",
messages=[
{"role": "system", "content": "You are an AI research assistant. You use a tone that is technical and scientific."},
{"role": "user", "content": "Hello, who are you?"},
{"role": "assistant", "content": "Greeting! I am an AI research assistant. How can I help you today?"},
{"role": "user", "content": "Can you tell me about the creation of black holes?"}
]
)
ChatGPT also supports single-turn tasks, similar to those used with text-davinci-003. This means developers can use ChatGPT to perform tasks previously demonstrated for the original GPT models, such as question-answering tasks.
Here's an example of a single-turn task using ChatGPT:
import openai
openai.ChatCompletion.create(
model="gpt-3.5-turbo",
messages=[
{"role": "system", "content": "You are an AI research assistant. You use a tone that is technical and scientific."},
{"role": "user", "content": "Hello, who are you?"},
{"role": "assistant", "content": "Greeting! I am an AI research assistant. How can I help you today?"},
{"role": "user", "content": "Can you tell me about the creation of black holes?"}
]
)
temperature=0, )
An effective prompt should be clear, concise, and focused. Here are some tips to help you create better prompts:
According to the official OpenAI docs, snapshots of the gpt-3.5-turbo model will be made available, allowing developers to choose specific model versions. This means that best practices for instructing models may change from version to version. The current recommendation for gpt-3.5-turbo-0301 is to add instructions in the user message instead of the available system message.
To learn more about how to make calls to the ChatGPT APIs and use its features, explore the following Python notebooks and resources:
openai library.With the rapid development of ChatGPT, it's essential to stay up-to-date with the latest techniques and approaches to prompt engineering. One of these approaches is automatic prompt design, which involves using machine learning to create prompts that improve the performance of language models.
This method helps reduce the manual effort required in prompt engineering, allowing developers to focus on other aspects of their applications. Automatic prompt design can be achieved using techniques like reinforcement learning and optimization algorithms to generate high-quality prompts that lead to better responses from language models.
To conclude, learning prompt engineering is a valuable skill for developers working with AI models like ChatGPT. As the field continues to evolve, it's essential to stay informed about the latest techniques, applications, and limitations. By doing so, you can create engaging, informative, and dynamic chatbots or conversational systems that make the most of the capabilities provided by ChatGPT.
By mastering prompt engineering, developers can unlock the full potential of AI language models like ChatGPT, creating engaging, informative, and dynamic conversational systems. As the field of AI continues to evolve rapidly, staying up-to-date with the latest techniques, applications, and limitations is essential. This comprehensive guide provides the foundation you need to excel in prompt engineering and create powerful AI-driven applications that offer exceptional user experiences.