Prompt Engineering Tutorial – Master ChatGPT and LLM Responses

This paragraph introduces the concept of prompt engineering, a field that has emerged with the rise of artificial intelligence. It involves the crafting and optimization of prompts to enhance the interaction between humans and AI systems. The speaker, Anu Kubo, a software developer and instructor, outlines the course content, which includes understanding AI, large language models (LLMs), and various prompt engineering techniques. The importance of prompt engineering is highlighted by its high demand and lucrative salaries in the job market.

The speaker explains the basics of artificial intelligence (AI), emphasizing that it simulates human intelligence processes without being sentient. AI relies on machine learning, which uses training data to identify patterns and make predictions. The paragraph delves into the role of language models, which are programs trained on vast amounts of text to understand and generate human-like responses. Examples of applications for language models are given, including virtual assistants and customer service chatbots.

This section provides a historical overview of language models, starting with Eliza, an early natural language processing program from the 1960s. It discusses the evolution of AI systems, from Eliza's pattern matching to the development of more advanced models like Shudlu and GPT. The paragraph highlights the significance of deep learning and neural networks in the advancement of language models, culminating in the powerful GPT-3 and GPT-4 models.

The speaker emphasizes the importance of having the right mindset for prompt engineering, comparing it to conducting effective Google searches. The paragraph discusses the process of creating prompts and the iterative nature of refining them for better results. It also touches on the importance of using the chat GPT platform and understanding the concept of tokens, which are the units of text that determine the cost of using the AI service.

The paragraph focuses on the best practices for writing effective prompts, including providing clear instructions, adopting a persona, specifying the format, using iterative prompting, avoiding leading answers, and limiting the scope for broad topics. Examples are given to illustrate how specific prompts can yield more accurate and useful responses from AI models.

This section discusses the benefits of adopting a persona when crafting prompts, which helps in creating responses that are relevant and consistent with the target audience's needs. The speaker provides examples of how prompts can be improved by specifying a persona, such as writing a poem for a sister's high school graduation with a specific style in mind.

The speaker introduces advanced prompting techniques such as zero-shot prompting, where the AI uses its pre-trained knowledge to answer questions without additional examples, and few-shot prompting, where a few examples are provided to enhance the AI's response. The paragraph provides practical examples of how these techniques can be applied to improve the interaction with AI models.

This paragraph explores the concept of AI hallucinations, which occur when AI models produce unusual or inaccurate outputs due to misinterpretation of data. The speaker also introduces the topic of text embeddings, explaining how textual information is converted into vectors that capture semantic meanings, allowing for more accurate processing by AI models. The paragraph concludes with a brief guide on how to create text embeddings using the OpenAI API.

The final paragraph wraps up the course on prompt engineering, summarizing the key topics covered, including an introduction to AI, linguistics, language models, prompt engineering mindset, best practices, zero-shot and few-shot prompting, AI hallucinations, and text embeddings. The speaker encourages viewers to explore and practice the concepts learned to harness the power of AI models effectively.