Prompt Engineering Tutorial – Master ChatGPT and LLM Responses

Anu Kubo introduces the course on prompt engineering, emphasizing its significance in maximizing productivity with large language models (LLMs). She outlines the course's coverage, including the basics of AI, an overview of LLMs like chat GPT, text-to-image models, and other emerging models. The course aims to teach the audience about prompt engineering, its importance, and the high demand and salaries for professionals in this field. Kubo explains that prompt engineering involves crafting prompts to refine interactions between humans and AI, requiring continuous monitoring and updating of prompts. The course will cover best practices, various types of prompting, and an introduction to chat GPT.

The paragraph demonstrates the utility of prompt engineering in language learning. It illustrates how AI, with the correct prompts, can act as a personalized English teacher, providing tailored responses, corrections, and engaging questions to keep learners interested. The example shows the transformation from a basic correction prompt to a more interactive and structured learning experience, highlighting the importance of clear and specific prompts to achieve the desired outcomes from AI.

This section underscores the importance of linguistics in prompt engineering, detailing various subfields of linguistics and their relevance to understanding language use and crafting effective prompts. It introduces language models as programs that learn from vast text collections to generate human-like text. The paragraph discusses the evolution of language models, starting from Eliza in the 1960s to modern models like GPT-4, highlighting the advancements in natural language processing and the growing capabilities of AI in understanding and generating human language.

The paragraph provides a historical overview of language models, starting with Eliza, an early AI program that simulated conversation, and progressing through the decades to modern models like GPT-3 and GPT-4. It discusses the development of language models in the context of AI's growing ability to process and generate human language, emphasizing the impact of deep learning and neural networks on the field. The paragraph also touches on the prompt engineering mindset, comparing the skill of crafting effective prompts to designing Google searches, and encourages the audience to think strategically about their prompts.

Anu Kubo offers a practical introduction to using OpenAI's chat GPT, guiding viewers through the process of signing up, logging in, and interacting with the platform. She demonstrates how to initiate and build on conversations with the AI, explaining the concept of tokens and their role in the platform's usage and billing. The paragraph also mentions the use of the API for more advanced interactions and points viewers to additional resources for learning more about OpenAI's offerings.

This section outlines best practices for effective prompt engineering, emphasizing the need for clear, detailed instructions and avoiding assumptions about the AI's knowledge. It provides examples of how specific prompts can yield more accurate and efficient responses, discusses the importance of adopting personas to tailor AI responses, and highlights the benefits of specifying the desired format for responses. The paragraph encourages a thoughtful approach to prompt construction to optimize interactions with AI.

The paragraph explores the use of persona and format specification in prompt engineering. It demonstrates how adopting a persona, such as a specific character or style, can enhance the relevance and quality of AI-generated content. Examples are given to show how a poem written for a high school graduation can be significantly improved by specifying the persona and writing style. The paragraph also discusses the importance of format specification, such as summaries or lists, to ensure the AI provides responses in the desired structure.

This section delves into advanced prompting techniques like zero-shot and few-shot prompting. Zero-shot prompting utilizes the pre-trained knowledge of a model to answer queries without additional examples, while few-shot prompting provides the model with a few examples to enhance its performance on a task. The paragraph gives practical examples of both techniques, showing how they can be used to elicit more accurate or tailored responses from AI models like GPT-4.

The final paragraph introduces the concept of AI hallucinations, which occur when AI models produce unusual outputs due to misinterpretations of data. It contrasts this with the more structured process of text embeddings, where textual information is converted into a high-dimensional vector for algorithmic processing. The paragraph provides an overview of text embeddings in the context of prompt engineering and offers guidance on using OpenAI's create embedding API to generate and compare text embeddings.

Anu Kubo concludes the course by recapping 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. She encourages viewers to apply what they've learned and looks forward to connecting with them again on the FreeCodeCamp channel.