Course on Prompt Engineering with Anu Kubo

Introduction

• Instructor: Anu Kubo, software developer and course creator
• Objective: Maximize productivity with large language models (LLMs) through prompt engineering
• Attractive Salaries: Up to $335,000/year (Bloomberg)
• Prerequisite: No coding background required

Course Outline

1. Introduction to Prompt Engineering
2. Brief Introduction to AI
3. Large Language Models (LLMs)
4. Text-to-Image Models like MidJourney
5. Emerging Models (Text-to-Speech, Text-to-Audio, Speech-to-Text)
6. Prompt Engineering Mindset
7. Best Practices
8. Zero-shot and Few-shot Prompting
9. Chain of Thought
10. AI Hallucinations
11. Vectors and Text Embeddings
12. Introduction to ChatGPT

What is Prompt Engineering?

• Definition: Writing, refining, and optimizing prompts to perfect human-AI interaction.
• Responsibilities:
  • Continuous monitoring and effectiveness checks
  • Maintaining an up-to-date prompt library
  • Reporting findings and being a thought leader

Introduction to AI

• Artificial Intelligence: Simulation of human intelligence by machines (not sentient)
• Machine Learning: Uses large amounts of training data to find patterns and predict outcomes
• Example: Categorizing paragraphs in different categories based on patterns

Large Language Models (LLMs)

• Capabilities: Generate realistic text, images, music, and other media
• Importance: Architects of AI struggle to control unpredictable outputs
• Example: Different responses to English learning prompts based on prompt specificity

Linguistics: Study of Language

• Phonetics: Speech sounds production and perception
• Phonology: Sound patterns and changes
• 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 human language
• Physio-linguistics: Human language acquisition and use
• Relation to Prompt Engineering: Understanding nuances crucial for crafting effective prompts

History of Language Models

• Eliza (1960s): Early natural language processing by Joseph Weisenbaum
• Shudlu (1970s): Interacted with a virtual world of blocks
• GPT Evolution: From GPT-1 (2018) to GPT-3 (2020) and recent advancements like GPT-4

Prompt Engineering Mindset

• Effective Google Searches Analogy: Understand how to write effective prompts to save time and improve results

Using ChatGPT

• Sign-up and Login: OpenAI website
• Interacting with GPT-4: Basic queries and building on previous conversations
• API Usage: Get API key for custom platform development
• Tokens: Processing text in chunks, costs associated with usage

Best Practices

• Clear Instructions: Write detailed and specific queries
• Adopt a Persona: Engage the AI to respond in a defined character
• Specify Formats: Summarizations, lists, detailed explanations, etc.
• Iterative Prompting: Continue questions to refine answers further
• Avoid Leading Questions: Prevent bias in AI responses
• Limit Scope: Break down broad topics into focused queries

Advanced Topics

• Zero-shot Prompting: Providing no specific examples
• Few-shot Prompting: Providing few specific examples to shape responses
• Examples: Setting dietary preferences and recommended restaurants

AI Hallucinations

• Definition: Unusual outputs due to misinterpretation of data (e.g., Google's Deep Dream)

Vectors and Text Embeddings

• Text Embedding: Represents text in high-dimensional vectors for algorithm processing
• Usage: Semantic meaning representation for better AI responses
• API: OpenAI's create embedding API

Conclusion

• Recap: Overview of topics
• Practical Skills: Introduced prompt engineering, AI fundamentals, using ChatGPT, best practices, and advanced concepts like embeddings.