Advanced Topics and Research in Prompt Engineering

Duration: 5 min

This module delves into advanced techniques in prompt engineering, exploring zero-shot, few-shot, Chain-of-Thought, ReAct, and system prompts. It also covers strategies for defending against prompt injection attacks. Understanding these concepts is crucial for leveraging the full potential of AI models in complex tasks.

Zero-shot and Few-shot Learning

Zero-shot learning allows a model to perform tasks it hasn't been explicitly trained on, by leveraging its understanding of language and context. Few-shot learning enhances this by providing a small number of examples to guide the model. These techniques are vital for adapting models to new tasks with minimal data.

from transformers import pipeline

# Zero-shot classification
classifier = pipeline("zero-shot-classification")

# Example input
hypothesis = "This is a sentence about climate change."
candidate_labels = ['environment', 'politics', 'economy']

# Perform zero-shot classification
result = classifier(hypothesis, candidate_labels)
print(result)

Try it in Google Colab:

{'sequence': 'This is a sentence about climate change.', 'labels': ['environment', 'politics', 'economy'],'scores': [0.983, 0.009, 0.008], 'average': None}

Chain-of-Thought (CoT) and ReAct Prompting

Chain-of-Thought prompting encourages models to provide reasoning steps before arriving at an answer, enhancing their problem-solving capabilities. ReAct (Reason and Act) prompting involves the model reasoning about a task and then taking an action, simulating a more interactive and dynamic problem-solving process.

from transformers import pipeline

# Text generation pipeline
generator = pipeline('text-generation')

# CoT prompt
prompt = "Let's think step by step: What is the capital of France? The capital of France is Paris."

# Generate text
result = generator(prompt, max_length=50)
print(result[0]['generated_text'])

💡 Tip: When using CoT prompting, ensure that the intermediate steps are clear and logically lead to the final answer to improve the model's performance.

Key Concepts

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