Course Recap and Next Steps

Duration: 5 min

This module provides a comprehensive recap of the key concepts covered in the course on prompt engineering, including zero-shot, few-shot, Chain-of-Thought, ReAct, system prompts, and prompt injection defense. Understanding these concepts is crucial for effectively leveraging large language models in various applications and ensuring their secure and reliable operation.

Zero-shot and Few-shot Prompting

Zero-shot prompting involves providing a model with a task without any specific examples, relying solely on the model's pre-trained knowledge. Few-shot prompting, on the other hand, provides the model with a small number of examples to guide its responses. These techniques are essential for adapting pre-trained models to new tasks with minimal data.

from transformers import pipeline

# Zero-shot classification
classifier = pipeline("zero-shot-classification")
result = classifier("This is a sentence about machine learning.", candidate_labels=["education", "technology", "health"])
print(result)

Try it in Google Colab:

{'sequence': 'This is a sentence about machine learning.', 'labels': ['technology', 'education', 'health'],'scores': [0.942, 0.041, 0.017], 'best_score': 0.942}

Chain-of-Thought (CoT) and ReAct Prompting

Chain-of-Thought prompting encourages models to generate intermediate reasoning steps before arriving at a final answer, enhancing the model's ability to solve complex problems. ReAct (Reason + Act) prompting involves guiding the model to reason about a task and then perform an action based on that reasoning, useful for tasks requiring multi-step problem-solving.

from transformers import pipeline

# Chain-of-Thought example
cot_pipeline = pipeline("text-generation")
cot_result = cot_pipeline("To solve 23 * 7, first multiply 20 by 7 to get 140, then multiply 3 by 7 to get 21, and finally add 140 and 21 to get 161. So, 23 * 7 = ")
print(cot_result[0]['generated_text'])

💡 Tip: When using Chain-of-Thought prompting, ensure that the intermediate steps are clear and logically connected to avoid confusion and improve the model's performance.

Key Concepts

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