What will you learn in All You Need to Know About Prompt Engineering Course
Core prompt engineering techniques: Understand prompt types (zero-shot, few-shot, chain-of-thought), prompt structure, and key design elements for guiding LLMs effectively.
Prompt optimization & evaluation: Learn how to craft, iterate, evaluate, and refine prompts—balancing length, clarity, context ordering, and role prompting techniques.
Role & productivity prompts: Apply prompt frameworks for different roles (developer, marketer, educator) and use cases like resume writing, emails, and interviewing
Preprocessing & output validation: Understand how data prep (e.g. examples, formatting) and output evaluation metrics contribute to robust prompt pipelines.
Program Overview
Module 1: Introduction to Prompt Engineering
⏳ ~30 minutes
Topics: Definition, history, importance, and evolution of prompts in generative AI.
Hands-on: Quiz on prompt types, examples, and roles.
Module 2: Crafting Effective Prompts
⏳ ~1 hour
Topics: Types of prompts (zero/few-shot, chain-of-thought), formatting, specificity, and clarity guidelines.
Hands-on: Design multiple prompts for given tasks and compare outputs.
Module 3: Techniques & Evaluation
⏳ ~1 hour
Topics: Techniques like CoT, tree-of-thought, prompt templating, parameter control.
Hands-on: Iteratively refine prompts and assess results across metrics.
Module 4: Role-Based Prompt Use Cases
⏳ ~45 minutes
Topics: Tailoring prompts for productivity—resumes, emails, interview prep, code assistance.
Hands-on: Create role-specific prompt templates and test across LLM responses.
Module 5: Best Practices & Deployment
⏳ ~45 minutes
Topics: Data preparation, handling hallucinations, prompt libraries, reuse, and versioning.
Hands-on: Build a mini prompt library and document prompt variations and performance.
Module 6: Final Quiz & Next Steps
⏳ ~15 minutes
Topics: Consolidation, critical skills, next projects.
Hands-on: Quiz on design concepts and final reflection.
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Job Outlook
Emerging LLM specialization: Foundation for roles like Prompt Engineer, AI Product Specialist, and AI-Assisted Developer.
Cross-domain applicability: Useful for devs, PMs, content creators, analysts, educators involving LLM-integrated workflows.
Industry demand: Growing as businesses embed AI into workflows—skilled prompt engineers are in high demand.
Portfolio readiness: Role-based templates and performance metrics provide substantial portfolio or interview-speak examples.
Specification: All You Need to Know About Prompt Engineering
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