Prompt Engineering Frameworks & Methodologies

Master Proven Techniques to Design, Tune, and Evaluate High-Performing Prompts for LLMs

What you'll learn

Discover the core principles of prompt engineering and why structured prompting leads to more consistent LLM outputs

Explore best practices and reusable templates that simplify prompt creation across use cases

Master foundational prompting frameworks like Chain-of-Thought, Step-Back, Role Prompting, and Self-Consistency.

Apply advanced strategies such as Chain-of-Density, Tree-of-Thought, and Program-of-Thought to handle complex reasoning and summarization tasks.

Design effective prompts that align with different task types—classification, generation, summarization, extraction, etc.

Tune hyperparameters like temperature, top-p, and frequency penalties to refine output style, diversity, and length.

Control model responses using max tokens and stop sequences to ensure outputs are task-appropriate and bounded.

Implement prompt tuning workflows to improve model performance without retraining the base model.

Evaluate prompt effectiveness using structured metrics and tools like PromptFoo for A/B testing and performance benchmarking.

Requirements

No prior experience or technical skills are required—just bring your curiosity, a computer with internet access, and an interest in exploring AI prompting.

Description

Course Description: Prompt Engineering Frameworks & MethodologiesIf you are a developer, data scientist, AI product manager, or anyone driven to unlock the full power of large language models, this course is designed for you. Ever asked yourself, “Why does my AI model misunderstand my instructions?” or “How can I write prompts that consistently get optimal results?” Imagine finally having the confidence to guide LLMs with precision and creativity, no matter your project."Prompt Engineering Frameworks & Methodologies" offers a deep dive into practical, cutting-edge techniques that go far beyond basic AI interactions. This course equips you to systematically design, evaluate, and tune prompts so you reliably unlock the most capable, nuanced outputs – whether you're building chatbots, automating workflows, or summarizing complex information.In this course, you will:Develop a working knowledge of foundational and advanced prompting strategies, including Chain-of-Thought, Step-Back, and Role Prompting.Master the use of prompt templates for consistency and efficiency in prompting design.Apply advanced thought structures such as Tree-of-Thought, Skeleton-of-Thought, and Program-of-Thought prompting for more sophisticated reasoning and output control.Fine-tune prompt hyperparameters like temperature, top-p, max tokens, and penalties to precisely steer model behavior.Implement real-world prompt tuning techniques and best practices for robust, repeatable results.Evaluate prompt output quality using industry tools (such as PromptFoo) to ensure your prompts achieve measurable results.Why dive into prompt engineering now? As AI models become increasingly central to business and research, crafting effective prompts is the skill that distinguishes average results from true excellence. Mastering these frameworks saves time, boosts model performance, and gives you a competitive edge in the rapidly evolving AI landscape.Throughout the course, you will:Create and iterate on custom prompt templates for varied tasks.Experiment hands-on with multiple prompting frameworks and document their effects.Tune and compare multiple prompt configurations for optimal model responses.Conduct structured evaluations of your prompt designs using real-world benchmarks and tools.This course stands apart with its comprehensive, methodical approach—grounded in the latest LLM research and hands-on industry application. Whether you're aiming to optimize a single task or architect complex multi-step workflows, you'll gain practical frameworks and actionable methodologies proven to work across the latest LLMs.Don’t just “use” AI—master the art and science of guiding it. Enroll now to transform your prompt engineering from guesswork into a powerful, repeatable craft!

Overview

Section 1: Introduction

Lecture 1 Prompting best practices

Lecture 2 Prompt templates

Section 2: Prompting frameworks

Lecture 3 Chain-of-thought prompting

Lecture 4 Step-back prompting

Lecture 5 Role prompting - does it even work?

Lecture 6 Self-consistency

Lecture 7 Chain-of-Density for better summaries

Section 3: Thought structures

Lecture 8 Tree-of-thought prompting

Lecture 9 Skeleton-of-thought prompting

Lecture 10 Program-of-thought prompting

Section 4: Prompt hyperparameters and their tuning

Lecture 11 What are prompt hyperparameters

Lecture 12 Temperature and top-p

Lecture 13 Max tokens and Stop sequence for controlling length of output

Lecture 14 Presence penalty and frequency penalty for variety in response

Lecture 15 Tuning prompt parameters

Section 5: Prompt tuning

Lecture 16 What is prompt tuning

Lecture 17 Process of implementing prompt tuning

Section 6: Conclusion

Lecture 18 About your certificate

Lecture 19 Bonus Lecture

AI developers who want to design more accurate and consistent prompts for language models.,Product managers who want to improve the performance and reliability of GenAI features in their applications.,Data analysts who want to extract better insights from LLMs using structured and optimized prompts.,Prompt engineers and hobbyists who want to go beyond trial-and-error and use proven prompting methodologies.,Researchers interested in exploring the frontiers of LLM prompting techniques and methodologies.,Technical writers or content creators intent on crafting better AI-assisted workflows and automations.