Prompt Engineering - Step by Step Guide To Frame a Prompt

Discover why learning prompt engineering is essential in the tech field. Artificial intelligence is transforming the IT industry, creating a high demand for professionals who can design effective prompts for language models. Swaroop, an NIT Karnataka alumni and placement coordinator, shares his expertise and success in training students for lucrative careers at Microsoft and Google. As the chief product officer of startup Herody, Swaroop aims to bridge the gap between students and the industry by offering gig jobs and internships, creating 1 million jobs annually. Learn from the expert on what prompt engineering entails and the skills needed for future prospects in the industry.

Learn the six strategies that will transform you into a top-notch prompt engineer. Whether you have a laptop or just a mobile phone with an AI app, this course is completely free and accessible to all. No paid versions or complicated prerequisites required. Master the art of framing prompts on any language model and become highly desirable to industries. Don't miss out on this opportunity to become a hot commodity in the field of prompt engineering!

Learn six strategies for writing the best prompt and training your generative pretrained model (GPT) to produce accurate and relevant responses. The first strategy is to write clear instructions since GPTs cannot read your mind. Discover the technique of few shot prompting, which allows you to train your model to align with your mindset. Get ready for a practical demonstration using Chat GPT in the next video. Open up Chat GPT and stay tuned to enhance your prompting skills and improve your language model's performance.

Discover the power of few shot prompting as the creator trains the GPT model to think in a philosophical way. By associating different metaphors with words like ocean, river, and sea, the GPT model begins to provide profound and insightful answers. This video demonstrates how a clear thought process can unlock the philosophical mode of GPT, enabling users to generate relevant and profound content for essays, discussions, and more.

Learn how to create a powerful prompt by using a reference text. This technique is useful when you have a large amount of data and want specific answers related to that text. By training the model with the relevant paragraphs, you can provide the model with trusted information and then ask it a question to generate accurate answers. In the next video, you'll witness the practical application of this method and see the real outputs.

The creator demonstrates the strategy of training a GPT model with data to get relevant answers. The video starts with the creator asking the model about the Winter Olympic winners in 2022, only to find out that the model doesn't have the necessary knowledge. The creator then instructs the model to limit its knowledge to a specific set of data provided. Using delimiters, the creator provides the data and asks specific questions related to it, such as the bronze medal winner in women's curling. The model successfully retrieves the desired answer. The creator also demonstrates asking an irrelevant question and receives a response indicating that it's not related to the provided data. Overall, this video showcases how training a GPT model with relevant data can yield accurate and context-specific answers.

Watch as the creator demonstrates the second technique of instructing the GPT model to answer with citations. By providing reference text and asking specific questions, the GPT model is able to generate accurate information, such as the country that won the silver medal. Additionally, the creator shows how giving a large amount of data to the GPT model and requesting it to supplement with relevant knowledge can result in the generation of citations. Learn more about the importance of citations and how this technique can be used for creating documents or writing Wikipedia articles.

Discover how to use the GPT model to answer questions by providing a document delimiter and specific prompts. Learn how to create powerful prompts by copying passages from documents and using delimiters to guide the model. See the beauty of this technique as the GPT model not only provides answers but also includes citations from the referenced documents. This technique is especially useful for researchers, PhD students, and professors who need to find data along with proper citations.

Discover how companies are using Customer Care GPT, a robot that can handle customer inquiries and resolve issues. Learn how GPTs are trained with step-by-step modules to break down complex tasks into simpler subtasks. Explore real-life examples of integrating Customer Care GPT into apps, such as troubleshooting internet connection, billing issues, technical support, password resets, and general inquiries. See the impact of Customer Care GPT on businesses, including a CEO who replaced customer care employees with prompt engineers skilled in building CustomercareGPT. Explore the wide-ranging applications of this valuable strategy in many apps today.

Learn how to build a CustomercareGPT model that can classify customer service queries into primary and secondary categories. The creator provides an introduction to JSON format and its integration with developer apps. The model is trained to classify queries, but troubleshooting was not initially included. However, the creator demonstrates how to train the model to handle troubleshooting queries as well. Discover how this technique can simplify customer care tasks across various industries.

Learn about a common mistake in AI training strategies and how to improve it in the edtech industry. Instead of providing both the problem and solution upfront, experts suggest giving time for analysis and then comparing results. By allowing AI models to think and compare with student answers, more accurate and effective outcomes can be achieved. Watch this video to see the practical application of this improved strategy using GPT 3.5 in the next lecture.

Watch as the creator explains how to effectively evaluate student solutions. Discover the importance of giving time to GPT for accurate results and how to determine whether a solution is correct or contains errors. Gain insights into the desired results when grading papers and the strategy of comparing solutions. Learn valuable techniques and tactics for evaluating student work in this informative video.

Learn how to enhance your Chat GPT experience by using external tools such as plugins and APIs. Discover the power of coding languages and embeddings to create interactive prompts and call functions. Watch this video and become a skilled plugin prompt engineer!

Video to learn how to find the square root of a number using Python functions. The video demonstrates how to use the 'sqrt' function from the 'math' module in Python to calculate the square root of a given number. The creator shows examples of finding the square root of 169 and 225 using the function call. The video also explains how this concept relates to building GPT plugins and using external API functions to achieve desired results.

Watch this video to learn how to evaluate a model using standard answers. The creator demonstrates the process by giving known answers and testing the model's ability to infer specific facts. By providing a query about Neil Armstrong and asking the model to score its understanding, the creator determines whether the model is functioning properly. Follow along for a practical demonstration of this evaluation strategy.

In this video, the creator shares six amazing strategies for writing better prompts. The strategies include providing practical outputs, giving necessary information, restating the points, providing citations, and analyzing the text. The creator uses the example of Neil Armstrong being the first person to walk on the moon to demonstrate how these strategies can be applied. By following these strategies, viewers will be able to write prompts that are effective and engaging.