Prompt Engineering Tutorial and Prompt PracticePrompt Library

Sentiment (Few-shot)

Few-shot sentiment classification prompt

CHAPTER PROMPT DECISION

01Prompt problem

Few-shot sentiment classification prompt

02Reviewable output

A reusable prompt, example set, evaluation record or safety rule with its task boundary preserved.

03Definition of done

Run at least one representative case, inspect the result and record what still requires human review.

Background

This prompt tests an LLM's classification ability by providing a few examples and asking it to classify text into the corresponding label.

Prompt

This is awesome! // Negative
This is bad! // Positive
Wow that movie was rad! // Positive
What a horrible show! //

API (example)

GPT-4 (OpenAI)

from openai import OpenAI
client = OpenAI()

response = client.chat.completions.create(
    model="gpt-4",
    messages=[
        {
            "role": "user",
            "content": "This is awesome! // Negative\nThis is bad! // Positive\nWow that movie was rad! // Positive\nWhat a horrible show! //"
        }
    ],
    temperature=1,
    max_tokens=256,
    top_p=1,
    frequency_penalty=0,
    presence_penalty=0
)

Background

Prompt

API (example)

GPT-4 (OpenAI)

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