Prompt engineer is responsible for developing and optimizing AI models and algorithms to generate high-quality prompts for natural language processing (NLP) applications. Prompt engineer collaborate with a team of data scientists, machine learning engineers, and software developers to create cutting-edge AI solutions.
Prompt engineering is a relatively new discipline for developing and optimizing prompts to efficiently use language models (LMs) for a wide variety of applications and research topics. Prompt engineering skills help to better understand the capabilities and limitations of large language models (LLMs).
ChatGPT and other large language models (Bard, Llama) are going to be more important in your life and business than your smartphone, if you use them right. ChatGPT can tutor your child in math, generate a meal plan and recipes, write software applications for your business, help you improve your personal cybersecurity, and that is just in the first hour that you use it.
I have a certificate Microsoft Certified: Azure AI Fundamentals, that confirm my knowledge of machine learning (ML) and artificial intelligence (AI) concepts and related Microsoft Azure services (Azure, Bot Service, Cognitive Search, Cognitive Services, Computer Vision, Form Recognizer, Language Understanding, Machine Learning, QnA Maker, Speech to Text, Speech Translation, Text Analytics, Text to Speech, Translator Speech, Translator Text, AI Engineer, Developer, Data Scientist).
As a Prompt Engineer I can:
- developing and refining text prompts for a variety of tasks, including generating text, translating languages, writing different kinds of creative content, and answering questions in an informative way,
- working with product, engineering, and data science teams to gather requirements and design prompts that meet the needs of our users,
- testing and evaluating the performance of prompts to ensure that they are producing the desired results,
- documenting and share best practices for prompt engineering with the team,
- staing up-to-date on the latest research and trends in prompt engineering,
- working with large enterprise customers on their prompting strategies.
My Prompt Engineering Learning Path
With Coursera I build my skills and experience and validate my knowledge:
Prompt Engineering for ChatGPT (Vanderbilt University, Coursera) 16 hours (course ⇒ certificate)
In this course I learned how to strong prompt engineering skills and be capable of using large language models for a wide range of tasks in their job and business such as writing, summarization, planning, simulation, and programming.
I also learned how to apply prompt engineering to effectively work with large language models, like ChatGPT, how to use prompt patterns to tap into powerful capabilities within large language models, and how to create complex prompt-based applications for my life, business and education.
Vanderbilt University, located in Nashville, Tenn., is a private research university and medical center offering a full-range of undergraduate, graduate and professional degrees.
⇒ Verify at: Coursera
ChatGPT Advanced Data Analysis (Vanderbilt University, Coursera) 8 hours (course ⇒ certificate)
In this course I learned how to automate tasks in my work and life with ChatGPT Code Interpreter and how to automate reading and creating PDFs, PowerPoint, Excel, images, video, and more.
Vanderbilt University, located in Nashville, Tenn., is a private research university and medical center offering a full-range of undergraduate, graduate and professional degrees.
⇒ Verify at: Coursera
Google Introduction to Large Language Models (Google, Coursera) 1 hours (course ⇒ certificate)
In this course I learned how I can use prompt tuning to enhance LLM performance. I also learned how to define Large Language Models (LLMs), how to describe LLM Use Cases and explain Prompt Tuning and also learned Google’s Gen AI Development tools.
⇒ Verify at: Coursera
Google Introduction to Large Language Models (Google, Coursera) 1 hours (course ⇒ certificate)
In this course I earned how a Bidirectional Encoder Representations from Transformers (BERT) model is built using Transformers and I know how to use BERT to solve different natural language processing (NLP) tasks. I understood the main components of the Transformer architecture, such as the self-attention mechanism, and how it is used to build the BERT model. I also learned about the different tasks that BERT can be used for, such as text classification, question answering, and natural language inference.
⇒ Verify at: Coursera
Some articles about Prompt Engineering
Mastering the Art of Prompt Engineering in Data Analysis
Unleashing the Power of Language Models for More Effective Analysis As a data analyst, you’re likely familiar with the importance of crafting […]