Future of prompt engineering: multimodal prompting, automated optimization, prompt frameworks, intent specification, and the evolving role of tools.

How to evaluate prompt performance using methods like golden sets, A/B testing, and LLM-as-a-judge. Master iterative prompt refinement strategies.

Real-world prompt engineering case studies: customer service chatbots, code assistants, image generation for brands, and data analysis helpers.

Address common LLM challenges: mitigating hallucinations, reducing bias, preventing prompt injection attacks, and handling model refusals effectively.

Learn to tailor prompts for specific domains: generating text, writing code, creating images with tools like Midjourney, and performing data analysis.

Advanced prompt engineering techniques like Chain-of-Thought (CoT), RAG, Self-Consistency, System Messages, and Function Calling for complex tasks.

Core principles of effective prompting: clarity, precision, context, structure, examples (few-shot), formatting, and focus for better AI results.

Explore how AI architectures (Transformers, Diffusion models) influence prompt engineering. Learn context windows & tailor prompts for GPT, Claude, Midjourney.

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.

Integrate your Python skills in a capstone project. Get ideas (data analysis, games, APIs), learn planning steps, and apply OOP, file handling, testing etc.