Prompt Engineering Tutorial – Master ChatGPT and LLM Responses

Anu Kubo, a software developer and instructor, leads a course on prompt engineering to optimize interactions with large language models (LLMs) like ChatGPT. The course covers AI basics, the role of prompt engineers, and techniques such as zero-shot and few-shot prompting. Kubo emphasizes the importance of linguistics in crafting effective prompts and highlights the lucrative nature of the profession. She illustrates how AI can be guided to provide more accurate and engaging responses through detailed and persona-based prompting. The course also delves into AI hallucinations, text embeddings, and best practices for using LLMs effectively.

• Prompt engineering is a profession that emerged with the rise of artificial intelligence (AI).
• It involves writing, refining, and optimizing prompts to enhance human-AI interaction.
• Continuous monitoring and updating of prompts are crucial as AI technology evolves.
• Prompt engineers maintain prompt libraries and report findings, acting as thought leaders in the AI field.

"Prompt engineering in a nutshell is a career that came about off the back of the rise of artificial intelligence. It involves human writing, refining, and optimizing prompts in a structured way."

• This quote introduces the concept of prompt engineering, highlighting its role in enhancing AI interactions.

• AI simulates human intelligence processes but is not sentient.
• Machine learning is a subset of AI that uses training data to identify patterns and make predictions.
• AI models are trained to categorize and predict outcomes based on the patterns in the data.

"Artificial intelligence is the simulation of human intelligence processes by machines. I say simulation as artificial intelligence is not sentient, at least not yet anyways."

• The quote clarifies that AI simulates human intelligence but lacks true consciousness or self-awareness.

• Prompt engineering is vital due to AI's rapid and exponential growth.
• Even AI creators struggle to control its outputs, making precise prompting essential.
• Effective prompts can transform AI interactions, providing more meaningful and engaging responses.

"With the quick and exponential growing rise of AI, even the architects of it themselves struggle to control it and its outputs."

• This quote underscores the necessity of prompt engineering to manage AI's unpredictable outputs.

• A basic prompt can lead to limited AI responses, while a well-crafted prompt can enhance learning experiences.
• Example: Using Chat GPT's GPT-4 model to correct grammar and engage learners interactively.
• Adding specific instructions to prompts can result in more interactive and educational AI responses.

"With the correct prompts, we can actually create that with AI. So let's give it a go and let's write a prompt to do this."

• The quote demonstrates how strategic prompt crafting can significantly improve AI interaction quality.

• Linguistics is the study of language, covering areas like phonetics, syntax, semantics, and more.
• Understanding linguistics is crucial for effective prompt engineering, as it helps craft prompts that yield accurate AI responses.
• Standard grammar and language structures are essential for AI systems to return precise results.

"Linguistics is the study of language. It focuses on everything from phonetics, so the study of how speech sounds are produced and perceived."

• This quote introduces the field of linguistics and its relevance to crafting effective AI prompts.

• Language models are AI programs that understand and generate human-like text.
• They learn from vast collections of written text, becoming experts in conversation, grammar, and style.
• Language models analyze input sentences and generate predictions or continuations that mimic human responses.

"A language model is a clever computer program that learns from a vast collection of written text."

• The quote describes language models' ability to learn from extensive text data, enabling them to generate human-like responses.

• Language models are integral to various applications, including virtual assistants, chatbots, and creative writing tools.
• The evolution of language models began with Eliza in the 1960s, a program designed to simulate human conversation by mimicking a Rogerian psychotherapist.
• Eliza used pattern matching to analyze input and generate responses, creating an illusion of understanding.
• Despite its simplicity, Eliza captivated users and sparked interest in natural language processing, paving the way for more advanced systems.
• The development of language models continued with programs like Shudlu in the 1970s and advanced significantly with the introduction of deep learning and neural networks around 2010.
• The Generative Pre-trained Transformer (GPT) series by OpenAI marked significant advancements in language modeling, with GPT-1 in 2018, GPT-2 in 2019, and GPT-3 in 2020, followed by GPT-4.

"Eliza was designed to simulate a conversation with a human being. Eliza had a special knack for mimicking a Rogerian psychotherapist, someone who essentially listens attentively and asks probing questions to help people explore their thoughts and feelings."

• Eliza's design and function illustrated the early capabilities of language models to simulate human-like conversation.

"Eliza's impact was profound, sparking interest and research in the field of natural language processing. It paved the way for more advanced systems that could truly understand and generate human language."

• Eliza's influence on the field of natural language processing was significant, leading to further research and development.

"The arrival of GPT-3 marked a real turning point in terms of language models and AI."

• GPT-3 represented a major advancement in language models, showcasing unprecedented abilities in understanding and generating text.

• Effective prompt engineering is crucial for optimizing interactions with language models like GPT.
• Similar to designing effective Google searches, crafting the right prompts can save time and resources.
• Developing intuitive skills in prompt engineering can enhance the efficiency and effectiveness of using language models.

"I personally like the analogy of prompting to designing effective Google searches. There are clearly better and worse ways to write queries against the Google search engine that solve your task."

• The analogy highlights the importance of crafting precise and effective prompts, akin to search engine queries.

• ChatGPT, specifically GPT-4, is used for various interactions and can build on previous conversations.
• Users can create new chats, delete old ones, and interact with the model to receive responses.
• The platform allows users to explore different functionalities and integrate the API for custom applications.

"For this tutorial, we are going to be interacting with chat GPT for. So please go ahead and click on here and that will take you to the platform."

• The tutorial provides a step-by-step guide on using ChatGPT for various interactions.

• GPT-4 processes text in units called tokens, which are approximately four characters or 0.75 words for English text.
• Users are charged based on the number of tokens used, and tools are available to track token usage.
• Managing token consumption is essential for efficient use of the platform.

"GPT-4 essentially processes all texts in chunks called tokens. And this token is approximately four characters or 0.75 words for English text."

• Understanding how tokens work is crucial for managing interactions with GPT-4 and optimizing costs.

• Explanation of how tokens are counted in AI models using a simple example.
• Discussion on managing token usage and associated costs through account settings.

"What is four plus four. And with that piece of text, the total count of tokens is going to be six."

• Tokens are units of text that AI models use to process and generate responses. This example demonstrates how a simple query is broken down into tokens.

• Prompt engineering is not just about simple commands; it requires a strategic approach.
• Importance of writing clear and detailed instructions in queries to enhance AI response accuracy.
• Use of iterative prompting for complex queries to refine and improve responses.
• Avoiding leading questions that might bias AI responses.
• Limiting the scope of broad topics to obtain focused answers.

"The biggest misconception when it comes to prompt engineering is that it's an easy job with no science to it."

• Prompt engineering involves a thoughtful process of designing prompts to achieve desired results, contrary to the belief that it is straightforward.

• Importance of specificity in prompts to avoid ambiguity and ensure the AI understands the context.
• Example of refining a vague prompt to a specific one to get accurate results.
• Specificity helps in saving tokens and time by avoiding unnecessary follow-up questions.

"Instead of writing, when is the election, you could write, when is the next presidential election for Poland?"

• Providing clear and specific context in prompts prevents misunderstandings and ensures the AI provides the correct information.

• Using personas to tailor AI responses to specific styles or characters.
• Personas help in generating content that aligns with the user's needs and preferences.
• Example of using a persona to write a poem with a specific style.

"Write a poem as Helena. Helena is 25 years old and an amazing writer. Her writing style is similar to famous 21st-century poet Rupi Kaur."

• Adopting a persona helps in producing content that is stylistically consistent with the specified character, enhancing personalization and relevance.

• Importance of specifying the desired format of AI responses to match user expectations.
• Example of requesting a summary in bullet points with a word limit to ensure concise output.

"Specify to use bullet points to explain what this essay is about, making sure each point is no longer than 10 words long."

• Specifying format helps in obtaining information in a preferred structure, making it easier to digest and utilize.

• Effective prompt engineering involves clear instructions, adopting personas, and specifying formats.
• These strategies help in maximizing the utility of AI models by ensuring accurate, relevant, and personalized responses.

• Prompt engineering involves crafting inputs to guide AI models like GPT-4 to produce desired outputs.
• Various formats can be specified, such as summaries, lists, detailed explanations, and checklists.

"We can do a bunch of other things, including specifying if something is a summary, a list, or a detailed explanation."

• Specifying the format of the output helps in achieving the desired level of detail and structure in responses.

• Zero-shot prompting utilizes a pre-trained model's knowledge without additional examples.
• It allows the model to perform tasks without having seen specific examples during its training.

"Zero-shot prompting leverages a pre-trained model's understanding of words and concept relationships without further training."

• Zero-shot is effective for tasks where the model already possesses relevant knowledge.

• Few-shot prompting involves providing the model with a few examples to enhance its understanding.
• This method is beneficial when the model needs additional context to perform tasks accurately.

"Few-shot prompting enhances the model with training examples via the prompt avoiding retraining."

• Few-shot prompting helps tailor the model's responses by offering specific examples.

• AI hallucinations refer to unexpected outputs produced by AI models when they misinterpret data.
• These outputs can be enlightening as they reveal the model's data interpretation processes.

"AI hallucinations aren't just entertaining. They're also quite enlightening. They show us how our AI models interpret and understand data."

• Hallucinations demonstrate the creative connections AI models make based on their training data.

• Text embedding is a technique to represent text in a format that can be processed by algorithms.
• It involves converting text into high-dimensional vectors capturing semantic information.

"Text embedding is a popular technique to represent textual information in a format that can be easily processed by algorithms."

• Text embeddings help in finding semantically similar words or phrases in large text corpora.

• The OpenAI API allows for creating text embeddings, which can be used to compare and find similar texts.

"To create a text embedding of a word or even a whole sentence, check out the create embedding API here from OpenAI."

• Embeddings are vital for tasks requiring semantic understanding, such as finding related words.

• The course covered various aspects of prompt engineering, including AI basics, linguistics, language models, and prompting techniques.
• Emphasized the importance of understanding AI hallucinations and text embeddings for effective AI model utilization.

"We covered an introduction to AI as well as looked at linguistics, language models, prompt engineering mindset, using GPT-4, how to look at best practices as well as zero-shot and few-shot prompting, as well as AI hallucinations, vectors or text embeddings."

• The course provided a comprehensive overview of prompt engineering and its applications.