Name: Prompt Engineering Frameworks & Methodologies
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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.

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.

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