Prompt Engineering Course Notes

Jul 30, 2024

Prompt Engineering Course Notes

Instructor Introduction

  • Name: Anu Kubo
  • Role: Software Developer, Course Creator at FreeCodeCamp and personal channel.
  • Focus of course: Exploring prompt engineering strategies for effective interaction with large language models (LLMs).

Course Overview

  • Objective: To master prompt engineering to maximize productivity with AI.
  • Topics Covered:
    • Definition and importance of prompt engineering.
    • Introduction to AI and LLMs.
    • Various models: Text to image, text to speech, etc.
    • Best practices in prompt engineering.
    • Concepts: Zero-shot prompting, few-shot prompting, AI hallucinations, text embeddings.
    • Quick intro to Chat GPT.

What is Prompt Engineering?

  • Definition: Refers to the structured writing, refining, and optimizing of prompts to interact with AI.
  • Roles of a Prompt Engineer:
    • Continuously improve and monitor prompts.
    • Maintain an effective prompt library.
    • Report findings and lead in the AI space.

Understanding Artificial Intelligence (AI)

  • Definition: Simulation of human intelligence processes by machines (not sentient).
  • Machine Learning: A subset of AI focused on identifying patterns within large data sets (e.g., categorizing paragraphs).

Importance of Prompt Engineering

  • As AI technology advances, even creators face difficulty in controlling outputs.
  • The quality of prompts directly affects the quality of responses from AI.
  • Example: Using specific prompts can enhance learning experiences with language models (e.g., GPT-4).

Basics of Linguistics and Its Role in Prompt Engineering

  • Key Areas of Study:
    • Phonetics, Phonology, Morphology, Syntax, Semantics, Pragmatics, Historical Linguistics, Sociolinguistics, Computational Linguistics, Physiological Linguistics.
  • Role: Understanding language nuances aids in crafting effective prompts that yield accurate AI responses.

Language Models Overview

  • Definition: Programs that understand and generate human-like text through extensive training data.
  • Use Cases: Virtual assistants, customer service, content creation.

History of Language Models

  • Eliza (1960s): Early natural language processing program simulating conversation.
  • Evolution: Shudlu (1970s), then from 2010 onwards with the emergence of deep learning models (e.g., GPT series).
    • Key Versions:
      • GPT-1 (2018): Initial impressive language model.
      • GPT-2 (2019) and GPT-3 (2020): Major advancements with 175 billion parameters.
  • Latest Version: GPT-4 and other models like BERT.

Prompt Engineering Mindset

  • Approach prompts as structured queries similar to searches on Google.
  • Example by Mahail Eric: Prompting is akin to designing effective Google searches.

Using Chat GPT by OpenAI

  • Getting Started: Register on OpenAI and log in to use Chat GPT-4.
  • Tokens: Interaction with GPT-4 is based on tokens, where a token corresponds to a chunk of text.

Best Practices in Prompt Engineering

  1. Clear Instructions: Provide detailed prompts with specific requests.
  2. Adopt a Persona: Direct the AI's responses to a particular character or role.
  3. Iterative Prompting: Follow up for clarity or expand upon initial responses.
  4. Avoid Leading Questions: Do not bias the AI’s responses by suggesting expected answers.
  5. Limit Scope: Keep prompts focused to receive precise answers.

Examples of Effective Prompting

  • Clear Instructions: Instead of vague queries, specify details (e.g., “When is the next presidential election for Poland?”)
  • Adopting a Persona: Guide AI in responding as a specific character (e.g., a teacher or professional).
  • Specifying Format: Clearly state the desired output format (e.g., bullet points, lists).

Advanced Prompting Techniques

  • Zero-shot Prompting: Asking questions without providing examples, relying on the model's existing knowledge.
  • Few-shot Prompting: Providing minimal examples to enhance the model’s performance on a task.

AI Hallucinations

  • Definition: Unusual outputs from AI models due to misinterpretation of data, often leading to inaccuracies.

Text Embeddings and Vectors

  • Concept: Techniques to represent text data in high dimensional vectors for better processing by AI.
  • Use in Prompt Engineering: Converting prompts to embeddings to capture semantic information.

Conclusion

  • Review of key concepts: Prompt engineering strategies, AI fundamentals, linguistic importance, and best practices for effective prompting.

This summary encapsulates the essential details and key takeaways from the lecture on prompt engineering.