Advanced Prompt Engineering Course

Advanced Prompt Engineering Course

Instructor: Priyanka Mehta

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3 modules

Gain insight into a topic and learn the fundamentals.

Beginner level

Recommended experience

7 hours to complete

3 weeks at 2 hours a week

Flexible schedule

Learn at your own pace

3 modules

Gain insight into a topic and learn the fundamentals.

Beginner level

Recommended experience

7 hours to complete

3 weeks at 2 hours a week

Flexible schedule

Learn at your own pace

What you'll learn

Craft effective prompts using structure, context, and output indicators
Apply core and advanced prompting techniques like CoT and ToT
Build dynamic, reusable prompts with LangChain, Jinja2, and Python f-strings
Design scalable GenAI workflows for real-world applications

Skills you'll gain

Details to know

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There are 3 modules in this course

This comprehensive Prompt Engineering course equips you with the skills to design, optimize, and scale effective prompts for generative AI and large language models. Begin by mastering the structure of prompts, learn how to use key elements like instructions, context, input data, and output indicators to generate precise outputs. Explore LLM settings and formatting techniques to enhance prompt effectiveness. Progress to core techniques such as zero-shot, few-shot, Chain of Thought (CoT), Self-Consistency, and Tree of Thoughts (ToT) prompting, reinforced with practical demos using OpenAI and LangChain. Learn to generate synthetic data for RAG models and create dynamic, reusable prompts using LangChain templates, Jinja2, and Python f-strings.

You should have a basic understanding of Python programming and familiarity with large language model outputs. By the end of this course, you will be able to: - Understand Prompts: Master structure and elements for accurate AI outputs - Apply Techniques: Use zero-shot, few-shot, CoT, and advanced strategies - Build Dynamically: Create reusable prompts with LangChain and templates - Scale with GenAI: Design prompt-driven workflows for real-world use cases Ideal for AI developers, data scientists, and professionals building GenAI-powered applications.

Master the foundations of prompt engineering with this hands-on module. Learn how to craft effective prompts, understand key elements like instructions, context, input data, and output indicators. Explore advanced techniques including LLM settings and prompt formatting for optimal results. Ideal for professionals looking to harness the power of generative AI tools efficiently.

What's included

8 videos1 reading4 assignments

8 videosTotal 36 minutes

Learning Objectives2 minutesPreview module
Introduction to Prompt Engineering2 minutes
Prompt Engineering: Example6 minutes
Introduction to Advanced Prompt Engineering1 minute
LLM Settings for Optimal Prompting7 minutes
Prompt Formatting: Crafting the Right Structure6 minutes
Instruction and Context in Prompt Elements4 minutes
Prompt Elements: Input Data and Output Indicator4 minutes

1 readingTotal 10 minutes

Course Syllabus 10 minutes

4 assignmentsTotal 85 minutes

Assessment for Foundations of Prompt Engineering40 minutes
Quiz on Prompt Engineering15 minutes
Quiz on Advanced Prompt Engineering15 minutes
Quiz on Prompt Elements15 minutes

Explore core prompting techniques to maximize the performance of large language models. Learn zero-shot, few-shot, and Chain of Thought (CoT) prompting to improve response accuracy and reasoning. Dive into advanced strategies like Self-Consistency and Tree of Thoughts (ToT) prompting with real-world demos using OpenAI and LangChain. Perfect for anyone mastering GenAI workflows.

What's included

14 videos6 assignments

14 videosTotal 76 minutes

Overview and Examples of Zero-Shot Prompting6 minutesPreview module
Demo: Zero-Shot Prompting with OpenAI11 minutes
Overview and Examples of Few-Shot Prompting7 minutes
Demo: Few-Shot Prompting with LangChain and OpenAI1 minute
Introduction to Chain of Thought (CoT) Prompting4 minutes
CoT Prompts for Better Reasoning3 minutes
CoT Technique: Examples6 minutes
Demo: Chain of Thought Prompting with LangChain and OpenAI6 minutes
Overview, Benefits and Features of Self-Consistency Prompting6 minutes
Self-Consistency Prompting: Examples3 minutes
Demo: Self-Consistency Prompting with LangChain and OpenAI3 minutes
Introduction to Tree of Thoughts (ToT) Prompting6 minutes
Tree of Thoughts (ToT) Prompting: Example3 minutes
Demo: Tree of Thoughts Prompting with LangChain and OpenAI3 minutes

6 assignmentsTotal 115 minutes

Assessment for Core Prompting Techniques40 minutes
Quiz on Prompting Techniques: Zero-Shot Prompting15 minutes
Quiz on Prompting Techniques: Few-Shot Prompting15 minutes
Quiz on Chain of Thought (CoT) Prompting15 minutes
Quiz on Self-Consistency Prompting Technique15 minutes
Quiz on Tree of Thoughts (ToT) Prompting15 minutes

Discover real-world applications and tools for effective prompt engineering. Learn how to generate synthetic data for RAG models and create powerful prompts using LangChain. Explore prompt templates, chat prompts, and dynamic message generation using Jinja2 and Python f-strings. This module is ideal for developers building GenAI-powered applications and custom LLM workflows.

What's included

12 videos3 assignments

12 videosTotal 58 minutes

Generating Synthetic Data for RAG Models4 minutesPreview module
Introduction to LangChain Prompts3 minutes
Prompt Templates5 minutes
Chat Prompt Template4 minutes
Custom Prompt Template2 minutes
Demo: Creating a Custom Template15 minutes
Template Formats1 minute
Demo: Using Jinja2 Template Format4 minutes
Demo: Using Python f-Strings Template Format4 minutes
Types of MessagePromptTemplate in LangChain1 minute
Demo: Dynamic Message Generation in LangChain10 minutes
Key Takeaways1 minute

3 assignmentsTotal 70 minutes

Assessment for Applications and Tools for Prompt Engineering40 minutes
Quiz on Major Applications of Prompt Engineering15 minutes
Quiz on LangChain Prompts15 minutes

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Instructor

Priyanka Mehta

Simplilearn

19 Courses304 learners

Offered by

Simplilearn

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Frequently asked questions

A prompt engineering course teaches you how to design effective prompts to get accurate and useful outputs from large language models like ChatGPT or Claude. It covers techniques, tools, and real-world applications.

The best course combines foundational concepts, hands-on demos with tools like OpenAI and LangChain, and teaches advanced techniques like Chain of Thought and Tree of Thoughts prompting.

Yes, a certificate demonstrates your ability to work with generative AI tools effectively—valuable for careers in AI development, data science, and product design.

Access to lectures and assignments depends on your type of enrollment. If you take a course in audit mode, you will be able to see most course materials for free. To access graded assignments and to earn a Certificate, you will need to purchase the Certificate experience, during or after your audit. If you don't see the audit option:

The course may not offer an audit option. You can try a Free Trial instead, or apply for Financial Aid.
The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.

When you purchase a Certificate you get access to all course materials, including graded assignments. Upon completing the course, your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile. If you only want to read and view the course content, you can audit the course for free.