Prompt Engineering Course by Anu Kubo

Introduction

• Instructor: Anu Kubo, software developer
• Focus: Prompt engineering strategies to improve interactions with AI tools like Chat GPT
• No coding background necessary
• Course Outline:
  • What is prompt engineering
  • Introduction to AI and Large Language Models (LLMs)
  • Text-to-image, text-to-speech, text-to-audio models
  • Prompt engineering mindset and best practices
  • Techniques like zero-shot prompting, few-shot prompting, chain of thought
  • Topics like AI hallucinations, text embeddings
  • Practical guide to using Chat GPT

What is Prompt Engineering?

• Definition: Career focused on writing, refining, and optimizing prompts for AI for effective interaction
• Responsibilities:
  • Write and monitor prompts
  • Maintain an up-to-date prompt library
  • Report findings and lead in the field

Introduction to AI

• AI: Simulation of human intelligence by machines (not sentient)
• Machine Learning: Uses large training data for pattern recognition to predict outcomes

Why is Prompt Engineering Useful?

• Helps control and guide AI outputs
• Example: Using Chat GPT to correct and enhance a student's writing with specific prompts

Basics of Linguistics

• Key Areas:
  • Phonetics: Study of speech sounds
  • Phonology: Sound patterns
  • Morphology: Word structure
  • Syntax: Sentence structure
  • Semantics: Linguistic meaning
  • Pragmatics: Language in context
  • Historical Linguistics: Language change
  • Sociolinguistics: Language and society
  • Computational Linguistics: Computers processing language
  • Physiological Linguistics: Human language acquisition and use
• Importance: Nuances of language critical for crafting effective prompts

Language Models

• Language Models: Programs that understand and generate human language
• Process: Analyze sentences, predict continuations, create human-like responses
• Applications: Virtual assistants, chatbots, creative writing
• Evolution:
  • Eliza (1960s): Early NLP program simulating a psychotherapist
  • Shudlu (1970s): Simple command understanding
  • GPT Series by OpenAI: GPT-1 (2018), GPT-2 (2019), GPT-3 (2020, 175 billion parameters), GPT-4 (latest)

Prompt Engineering Mindset

• Efficient prompt writing saves time and resources
• Analogy: Similar to effective Google searching
• Key Quote: Mahail Eric - Effective Google searches enhance prompt writing skills

Using Chat GPT

• Sign up on openai.com
• Interaction basics: Ask questions, build on previous conversations
• Tokens: Processed in chunks (approx. 4 characters each), manage usage and cost
• Best Practices:
  • Clear instructions
  • Adopting personas
  • Specifying format
  • Iterative prompting
  • Avoiding leading questions
  • Limiting scope for detailed topics

Advanced Prompting Techniques

• Zero-Shot Prompting: Using pre-trained model understanding without examples
• Few-Shot Prompting: Enhancing model with a few training examples via prompt

AI Hallucinations

• Unusual AI outputs due to data misinterpretation
• Example: Google's Deep Dream project
• Importance: Understanding AI's interpretation of data

Vectors and Text Embeddings

• Text Embedding: Representing text as high-dimensional vectors for processing by algorithms
• Semantic Meaning: Capturing meanings of words for better comparison and prediction
• Creating Text Embeddings: Using OpenAI's create embedding API

Conclusion

• Recap: Detailed overview of prompt engineering, AI introduction, linguistics, best practices, advanced techniques, and practical usage of Chat GPT
• Encouragement to experiment and practice prompt engineering for optimal AI interaction