Align prompt instructions with the task’s end goal

When engineering a prompt, we need to put ourselves in the shoes of the LLM and ask: what will the model think the end goal of my task is?

For example, if we want the model to respond to a customer with a single response in a friendly way, we might write a prompt like:

"This is a conversation between a customer and a polite, helpful customer support service agent."

This provides a clear and direct instruction to the model. However, we must consider what GPT-3 will think the task actually is.

Saying “this is a conversation” in a prompt makes GPT-3 think the task is to generate a full transcript of a conversation vs just giving a single response

GPT-3 will take things literally. Saying “this is a conversation” in the prompt makes GPT-3 makes GPT-3 generate a full dialogue (the most likely continuation) rather than just giving a single response

We can see that GPT-3 thought the task was to create an entire conversation transcript given the customer’s initial input and not simply to just respond once to the customer. GPT-3 was not wrong necessarily but it was misaligned with the original specific task intention. Let’s instead change our prompt to “Respond to a customer as a polite, helpful customer service agent.”.

Changing the prompt to say “Respond to ...” is more aligned with the end goal of what we wanted from the LLM.

Changing the prompt to say “Respond to …” is more aligned with the end goal of what we wanted from the LLM.

The small change from "this is a conversation…" to "respond to a…" aligned GPT-3's response with our desired outcome. Prompts must be both direct and tailored to the task; when they are not, the model will respond with what it believes is the correct task, regardless of our intention.

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