You have an Azure subscription. The subscription contains an Azure OpenAI resource that hosts a GPT-3.5 Turbo model named Model1.
You configure Model1 to use the following system message: “You are an AI assistant that helps people solve mathematical puzzles. Explain your answers as if the request is by a 4-year-old.”
Which type of prompt engineering technique is this an example of?
The prompt engineering technique used in this scenario is priming. Priming involves providing context or instructions to the model before it generates a response. In this case, the system message primes the GPT-3.5 Turbo model by setting the expectation that it should act as an AI assistant that explains mathematical puzzles in a manner understandable to a 4-year-old. This type of instruction helps guide the model's behavior and ensures that its responses align with the given context or instruction.
Priming is utilised in this example because it involves setting up the context or role of the AI model explicitly in a system message. It instructs the model about its role (“You are an AI assistant that helps people solve mathematical puzzles.") and also provides directions about how it should respond ("Explain your answers as if the requestor is a 4-year-old."). This essentially 'primes' the model for the conversation, as it lets the model know the expected behavior and persona that it needs to take on throughout the dialogue.
ChatGPT gave the same answer.
I'd go with "chain of thoughts", since it's about explaining the answers. Priming is a different story: "This refers to including a few words or phrases at the end of the prompt to obtain a model response that follows the desired form. For example, using a cue such as “Here’s a bulleted list of key points:\n- ” can help make sure the output is formatted as a list of bullet points." ...that's not the case here! https://learn.microsoft.com/en-us/azure/ai-services/openai/concepts/advanced-prompt-engineering?pivots=programming-language-chat-completions#prime-the-output
Priming is the answer https://learn.microsoft.com/en-us/azure/ai-services/openai/concepts/advanced-prompt-engineering?pivots=programming-language-chat-completions#system-message
A is the answer. A common way to adapt language models to new tasks is to use few-shot learning. In few-shot learning, a set of training examples is provided as part of the prompt to give additional context to the model. When using the Chat Completions API, a series of messages between the User and Assistant (written in the new prompt format), can serve as examples for few-shot learning. These examples can be used to prime the model to "respond in a certain way, emulate particular behaviors", and seed answers to common questions.
Are we reading the same question? There are no examples.
D. Priming Priming involves providing context or instructions to the model before it generates a response. In this case, the system message is priming the GPT-3.5 Turbo model by setting the expectation that it should provide explanations in a way that is understandable to a 4-year-old. This technique helps guide the model's behavior and output based on the given context or instruction.
Exam Question June 2024
I say this answer is D.
D is correct answer.
C is the answer : to explain it step by step Not priming, that manipulates the output
A. few-shot learning is right answer.
Where is the example? Few shot is: 2+2=4, 5+5=10. What is 4+4?
Must be priming, here we setting the role for the AI model
chain of thought. Try it on OpenAI. It explains step by step for a formular like 7+5*3+8