Future of Prompt Engineering
Future of prompt engineering: multimodal prompting, automated optimization, prompt frameworks, intent specification, and the evolving role of tools.
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Tutorials
Evaluation and Iteration
How to evaluate prompt performance using methods like golden sets, A/B testing, and LLM-as-a-judge. Master iterative prompt refinement strategies.
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Prompt Engineering Case Studies
Real-world prompt engineering case studies: customer service chatbots, code assistants, image generation for brands, and data analysis helpers.
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Overcoming Common Challenges
Address common LLM challenges: mitigating hallucinations, reducing bias, preventing prompt injection attacks, and handling model refusals effectively.
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Domain-Specific Prompting
Learn to tailor prompts for specific domains: generating text, writing code, creating images with tools like Midjourney, and performing data analysis.
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Advanced Prompting Techniques
Advanced prompt engineering techniques like Chain-of-Thought (CoT), RAG, Self-Consistency, System Messages, and Function Calling for complex tasks.
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Core Principles of Effective Prompting
Core principles of effective prompting: clarity, precision, context, structure, examples (few-shot), formatting, and focus for better AI results.
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Understanding AI Model Architectures
Explore how AI architectures (Transformers, Diffusion models) influence prompt engineering. Learn context windows & tailor prompts for GPT, Claude, Midjourney.
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Chapter 1: Introduction to Prompt Engineering
Learn the fundamentals of prompt engineering for AI models like GPT-4. Understand why prompting matters, its rise, and how it differs from traditional coding.
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Programming with Python | Chapter 25: Capstone Project
Integrate your Python skills in a capstone project. Get ideas (data analysis, games, APIs), learn planning steps, and apply OOP, file handling, testing etc.
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