Master Proven Techniques to Design, Tune, and Evaluate High-Performing Prompts for LLMs
What you will learn
Get Instant Notification of New Courses on our Telegram channel.
Noteβ Make sure your ππππ¦π² cart has only this course you're going to enroll it now, Remove all other courses from the ππππ¦π² cart before Enrolling!
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 typesβclassification, 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.
English
language