Welcome to the exciting world of AI and Prompt Engineering! This lesson will introduce you to the basics of artificial intelligence, especially large language models, and equip you with the fundamental knowledge of prompt engineering for content creation.
Artificial Intelligence (AI) refers to the simulation of human intelligence in machines that are programmed to think and act like humans. AI systems are designed to perform tasks that typically require human intelligence, such as learning, problem-solving, and decision-making. Examples you might be familiar with include virtual assistants like Siri and Alexa, recommendation systems on streaming services, and spam filters in your email.
To understand AI, let's visualize a simple example: Imagine you want an AI to write a haiku. The AI, if properly designed with the right models, can understand the context of the prompt and create a haiku based on it. This ability to process and interpret information is a core function of AI.
Key Takeaway: AI encompasses a wide range of technologies aimed at enabling machines to perform tasks that normally require human intelligence.
Large Language Models (LLMs) are a type of AI that are specifically designed to understand and generate human language. Think of them as sophisticated word processors on steroids! They are trained on massive datasets of text and code, allowing them to understand patterns, relationships, and structures within language. LLMs are the backbone of many content creation tools, capable of generating text, translating languages, writing different kinds of creative content, and answering your questions in an informative way.
Examples of LLMs include:
Key Takeaway: LLMs are powerful AI models that understand and generate human language, essential for content creation.
Prompt engineering is the art and science of crafting effective prompts to get the desired output from an AI model. A prompt is simply the input you give to the AI. The quality of your prompt directly impacts the quality of the AI's response. It's like giving instructions to a human assistant: the clearer and more specific your instructions are, the better the outcome.
Why is Prompt Engineering Important?
Example:
Notice how the second prompt is much more specific and provides more context.
Key Takeaway: Prompt Engineering is all about crafting effective instructions that will yield the desired output from your AI models. This improves both the quality and efficiency of your content creation.
Let's take a look at some AI model interfaces. You'll notice they all share some similarities: a space to input your prompt and a display area for the AI's generated response.
Activity: Visit the websites of OpenAI and Google AI, and familiarize yourself with the user interfaces. Get familiar with where to input text, submit the prompt, and view the AI's response.
Explore advanced insights, examples, and bonus exercises to deepen understanding.
Welcome back! Let's expand on the foundation we built today, delving deeper into the world of AI and Prompt Engineering. We'll explore more advanced concepts and practical applications.
Today, we learned about Large Language Models (LLMs) but let's explore the nuances. LLMs work by predicting the next word in a sequence, based on the patterns learned from massive datasets. Think of it like this: the model sees billions of examples of text and learns which words are likely to follow others. Different LLMs are trained on varied datasets, which influences their output. For instance, an LLM trained primarily on scientific papers might excel at technical writing, while one trained on social media data may understand colloquial language better. Understanding this training data is vital when selecting the right model for your prompt engineering tasks. Also, remember the context window. This is the limit of how many tokens (words or parts of words) an LLM can "remember" when responding to your prompt. Larger context windows allow for more complex interactions, like writing longer pieces or maintaining a consistent tone throughout.
Consider the role of parameters. These are the values that the LLM learns during training. The number of parameters is often used as a metric to define the size and capability of the model. More parameters generally mean the model can learn more complex patterns, but also require more computational resources. Different models utilize different architectures and learning techniques, each having its own strengths and weaknesses.
Research and list 3 different LLMs (e.g., OpenAI's GPT models, Google's PaLM, open-source models like Llama). Briefly describe what datasets each model was likely trained on and hypothesize what types of content they would excel at generating. Consider factors such as their purpose, the availability of the model, and the resources to access and operate it.
Choose an LLM. Write a prompt that is exactly 500 words. Then, using the same model, ask it to summarize the content of your prompt in 50 words. Did the summary accurately capture the main points? If the answer is no, try again while keeping in mind the context window. Then, try it with a prompt that is 1000 words. Reflect on the difference in output quality as prompt length increases.
LLMs are already transforming many industries:
Select an LLM. Write a prompt asking it to rewrite the following sentence, incorporating a specific writing style (e.g., "Write this in the style of Ernest Hemingway," "Rewrite this as a Shakespearean sonnet," "Rewrite this in a scientific journal style"). Submit your rewritten sentence with the prompt you used. Experiment with different writing styles. How consistent is the LLM? What limitations did you experience?
Write two prompts: (1) a bad prompt and (2) a good prompt to get an AI model to write a social media post about your favorite book. Compare the difference in the resulting outputs. *Choose a platform like OpenAI ChatGPT or Google AI Bard to execute your prompts*
Visit the websites for OpenAI and Google AI. Explore the interfaces. Ask the AI models the following questions: 'What is the capital of France?' and 'Write a haiku about the ocean.' Observe the different formats of answers and styles of the AI responses.
After trying your different prompts, discuss how the quality of the prompts impacts the AI-generated content. What did you learn about writing effective prompts?
Imagine you're starting a blog about healthy eating. Develop three different prompts to generate content ideas for your blog using an AI model. Test those prompts with an AI of your choice and document your findings, focusing on the differences in the output depending on prompt construction. Make sure to include a 'bad' prompt, and a 'good' prompt for the same topic.
Familiarize yourself with the specific user interfaces of the AI models you choose to use, like ChatGPT or Google Bard. Be ready to learn the components of a prompt and its parameters for next lesson.
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