Final Project: Comprehensive Prompt Engineering

Duration: 5 min

This module delves into the art and science of prompt engineering, a crucial skill for leveraging the full potential of large language models. You will learn about various prompting techniques such as Zero-shot, Few-shot, Chain-of-Thought, ReAct, and System Prompts. Additionally, you will explore strategies for defending against prompt injection attacks. By the end of this module, you will be equipped to design effective prompts that yield optimal results.

Zero-shot and Few-shot Prompting

Zero-shot prompting involves providing a model with a task without any examples, relying solely on the model's pre-trained knowledge. Few-shot prompting, on the other hand, provides the model with a few examples to guide its responses. Both techniques are essential for leveraging the model's capabilities in diverse scenarios.

from transformers import pipeline

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

# Example input
hypothesis = "The quick brown fox jumps over the lazy dog."
candidate_labels = ['animal', 'weather', 'technology']

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

Try it in Google Colab:

{'sequence': 'The quick brown fox jumps over the lazy dog.', 'labels': ['animal', 'weather', 'technology'], 'scores': [0.982, 0.003, 0.015], 'average': None}

Chain-of-Thought and ReAct Prompting

Chain-of-Thought (CoT) prompting encourages the model to provide intermediate reasoning steps before arriving at a final answer. ReAct prompting combines reasoning and action, allowing the model to perform tasks that require external tools or APIs. These techniques enhance the model's problem-solving capabilities.

from transformers import pipeline

# Chain-of-Thought prompting
cot_pipeline = pipeline('text-generation', model='google/t5-v1_1-large')

# Example input
prompt = "What is the capital of France? Let's think step by step: France is a country in Europe. The capital of France is Paris."

# Generate response using CoT
response = cot_pipeline(prompt, max_length=50)[0]['generated_text']
print(response)

💡 Tip: When using Chain-of-Thought 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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