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Google Cloud Training

This is a self-paced lab that takes place in the Google Cloud console.

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This is a self-paced lab that takes place in the Google Cloud console.

This lab focuses on the essentials and best practices of prompt design to help you learn how to design good quality prompts, how to interact with PaLM to get the responses you’re looking for, and how to be aware of hallucinations in responses.

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What's inside

Syllabus

Prompt Design using PaLM

Good to know

Know what's good
, what to watch for
, and possible dealbreakers
Uses a self-paced lab in the Google Cloud console, giving learners hands-on experience
Covers the essentials and best practices of prompt design
Provides guidance on interacting with PaLM to get desirable responses
Focuses on prompt design, which is crucial for effective AI interaction
Emphasizes awareness of hallucinations in responses, promoting critical thinking
Taught by Google Cloud Training, recognized for their expertise in cloud technologies

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Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Prompt Design using PaLM with these activities:
Follow tutorials on NLP basics
Enhance your understanding of core NLP concepts through guided tutorials covering tokenization, stemming, and part-of-speech tagging.
Show steps
  • Find tutorials on NLP basics
  • Complete the tutorials
  • Practice NLP tasks
Practice NLP exercises
Reinforce your learning by completing exercises focusing on NLP tasks like text classification and sentiment analysis.
Show steps
  • Find NLP exercises
  • Complete the exercises
  • Review your results
Attend NLP study group meetings
Connect with peers, exchange knowledge, and engage in discussions on NLP topics, fostering a collaborative learning environment.
Show steps
  • Find an NLP study group
  • Attend the meetings
  • Participate in discussions
Two other activities
Expand to see all activities and additional details
Show all five activities
Try out online NLP courses
Expand your knowledge through online NLP courses, delving deeper into specific aspects of the field and gaining a more comprehensive understanding.
Show steps
  • Find online NLP courses
  • Enroll in the courses
  • Complete the courses
Create a blog post on NLP use cases
Demonstrate your understanding by writing a blog post that explores real-world applications of NLP, showcasing your knowledge and critical thinking skills.
Show steps
  • Choose NLP use cases
  • Research the use cases
  • Write the blog post

Career center

Learners who complete Prompt Design using PaLM will develop knowledge and skills that may be useful to these careers:
Data Scientist
A Data Scientist analyzes data to extract insights and make predictions. This course may be useful for Data Scientists who want to learn how to design prompts that effectively interact with PaLM, a large language model, to get the responses they're looking for. This course can help Data Scientists improve the quality of their data analysis and make more accurate predictions.
Machine Learning Engineer
A Machine Learning Engineer designs, develops, and deploys machine learning models. This course may be useful for Machine Learning Engineers who want to learn how to design prompts that effectively interact with PaLM to enhance the performance and accuracy of their machine learning models.
Natural Language Processing Engineer
A Natural Language Processing Engineer develops and applies techniques to process and analyze natural language data. This course may be useful for Natural Language Processing Engineers who want to learn how to design prompts that effectively interact with PaLM to improve the accuracy and efficiency of their natural language processing tasks.
Software Engineer
A Software Engineer designs, develops, and maintains software systems. This course may be useful for Software Engineers who want to learn how to design prompts that effectively interact with PaLM to automate tasks, improve code quality, and increase productivity.
Product Manager
A Product Manager defines and manages the development of products. This course may be useful for Product Managers who want to learn how to design prompts that effectively interact with PaLM to gather user feedback, generate ideas, and make data-driven decisions.
Data Analyst
A Data Analyst collects, analyzes, and interprets data to identify trends and patterns. This course may be useful for Data Analysts who want to learn how to design prompts that effectively interact with PaLM to uncover insights and make better decisions.
Business Analyst
A Business Analyst analyzes business processes and identifies areas for improvement. This course may be useful for Business Analysts who want to learn how to design prompts that effectively interact with PaLM to gain insights into business operations and make recommendations for improvement.
UX Designer
A UX Designer designs user interfaces and experiences. This course may be useful for UX Designers who want to learn how to design prompts that effectively interact with PaLM to gather user feedback, improve user experience, and increase usability.
Content Writer
A Content Writer creates and edits written content. This course may be useful for Content Writers who want to learn how to design prompts that effectively interact with PaLM to generate ideas, write better content, and improve their storytelling skills.
Technical Writer
A Technical Writer creates and edits technical documentation. This course may be useful for Technical Writers who want to learn how to design prompts that effectively interact with PaLM to write clear and concise technical documentation.
Instructional Designer
An Instructional Designer creates and develops educational materials. This course may be useful for Instructional Designers who want to learn how to design prompts that effectively interact with PaLM to create engaging and effective learning experiences.
Project Manager
A Project Manager plans, executes, and closes projects. This course may be useful for Project Managers who want to learn how to design prompts that effectively interact with PaLM to manage projects, track progress, and communicate with stakeholders.
Marketing Manager
A Marketing Manager develops and executes marketing campaigns. This course may be useful for Marketing Managers who want to learn how to design prompts that effectively interact with PaLM to generate creative ideas, target the right audience, and track campaign performance.
Sales Manager
A Sales Manager leads and manages a sales team. This course may be useful for Sales Managers who want to learn how to design prompts that effectively interact with PaLM to identify potential customers, close deals, and build relationships.
Customer Success Manager
A Customer Success Manager ensures that customers are satisfied with a company's products or services. This course may be useful for Customer Success Managers who want to learn how to design prompts that effectively interact with PaLM to resolve customer issues, build relationships, and increase customer satisfaction.

Reading list

We've selected 11 books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in Prompt Design using PaLM.
Provides a comprehensive overview of language models, including their history, architectures, and applications. It valuable resource for anyone who wants to learn more about this rapidly evolving field.
Comprehensive overview of deep learning. It covers a wide range of topics, including neural networks, deep learning architectures, and applications.
Hands-on guide to deep learning. It covers a wide range of topics, including neural networks, deep learning architectures, and applications.
Practical guide to machine learning with Python. It covers a wide range of topics, including data preprocessing, feature engineering, model selection, and hyperparameter tuning.
Practical guide to machine learning with Python. It covers a wide range of topics, including data preprocessing, feature engineering, model selection, and hyperparameter tuning.
Provides a probabilistic perspective on machine learning. It covers a wide range of topics, including Bayesian inference, graphical models, and Gaussian processes.
Comprehensive overview of reinforcement learning. It covers a wide range of topics, including Markov decision processes, value functions, and reinforcement learning algorithms.
Comprehensive overview of deep reinforcement learning. It covers a wide range of topics, including deep neural networks, reinforcement learning algorithms, and applications.
Comprehensive overview of generative adversarial networks. It covers a wide range of topics, including GAN architectures, training techniques, and applications.
Comprehensive overview of machine learning. It covers a wide range of topics, including supervised learning, unsupervised learning, and reinforcement learning.

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