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Prompt Engineering - Notes

Table of Contents (ToC)

Introduction

Prompt engineering is the process of designing and optimizing prompts for AI models to elicit the desired responses.

What's Prompt Engineering?

  • Crafting input prompts to guide AI outputs.
  • Enhancing model performance with well-designed prompts.
  • A critical skill for interacting with AI systems effectively.

Key Concepts and Terminology

  • Prompt: The input given to an AI model.
  • Completion: The output generated by the AI model.
  • Tokens: Units of text the model processes.
  • Context: The surrounding text influencing the model's output.

Applications

  • Generating human-like text in chatbots.
  • Summarizing documents and articles.
  • Automating customer service responses.
  • Creating interactive AI-based tools.

Fundamentals

Prompt Engineering Architecture Pipeline

  • Defining the task and objectives.
  • Crafting initial prompts.
  • Iterative testing and refinement.
  • Evaluating model outputs for desired performance.

How Prompt Engineering Works?

  • Identifying the purpose and scope of the task.
  • Designing prompts with specific instructions.
  • Utilizing examples to guide model responses.
  • Refining prompts based on model feedback.

Prompt Engineering Techniques

  • Zero-shot: Providing no examples, relying on the model's general knowledge.
  • Few-shot: Giving a few examples to guide the model.
  • One-shot: Providing a single example to demonstrate the task.
  • Multi-shot: Offering multiple examples to improve response accuracy.
  • Chain-of-thought: Structuring prompts to show the reasoning process step-by-step.

Some Hands-on Examples

  • Text Summarization: Crafting prompts for concise summaries.
  • Conversational Agents: Designing prompts for engaging dialogues.
  • Content Generation: Creating prompts for writing assistance.

Tools & Frameworks

  • OpenAI GPT
  • Hugging Face Transformers
  • Google's T5
  • Microsoft DialoGPT

Hello World!

import openai

# Set up the OpenAI API key
openai.api_key = 'your-api-key'

# Define a simple prompt
prompt = "Explain the concept of prompt engineering in AI."

# Get the completion from the AI model
response = openai.Completion.create(
    engine="text-davinci-003",
    prompt=prompt,
    max_tokens=100
)

# Print the response
print(response.choices[0].text.strip())

Lab: Zero to Hero Projects

  • Project 1: Building a FAQ chatbot using prompt engineering.
  • Project 2: Creating a personalized email assistant.
  • Project 3: Developing a story generator with adjustable prompts.
  • Project 4: Designing an AI-driven content summarizer.

References

Free Course: - Course on ChatGPT Prompt Engineering for Developers - DeepLearningAI - Prompt Engineering for Vision Models - DeepLearningAI

Awesome ChatGPT Prompts:

Visual Prompting