Prompt Engineering Frameworks & Methodologies
Master Proven Techniques to Design, Tune, and Evaluate High-Performing Prompts for LLMs
4.43 (7 reviews)
166
students
3 hours
content
Jul 2025
last update
$19.99
regular price
What you will 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.
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6727239
udemy ID
18/07/2025
course created date
23/07/2025
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