Foundations of Generative AI
Explain the principles of Generative AI and how it differs from predictive AI.
Describe the architecture of LLMs (e.g., transformers, tokenization, embeddings).
Identify strengths and limitations of generative models, including issues like hallucination and bias.
Prompt Engineering Essentials
Define what makes a “good prompt” (clarity, specificity, context, constraints).
Apply prompt patterns such as role prompting, chain-of-thought, few-shot, and zero-shot prompting.
Diagnosing and refining prompts to improve accuracy, creativity, or compliance with task requirements.
Advanced Prompting Techniques
Leverage multi-step prompting for complex reasoning tasks.
Design modular prompts for workflows in coding, summarization, translation, and data analysis.
Integrate external tools and frameworks (e.g., LangChain, vector databases) to extend LLM capabilities.
Responsible & Ethical AI Use
Recognize ethical considerations in prompt design (bias mitigation, fairness, transparency).
Apply responsible prompting strategies to reduce harmful or misleading outputs.
Evaluate AI responses for trustworthiness and alignment with organizational or regulatory standards.
Real-World Applications
Develop domain-specific prompts for business, finance, education, and creative industries.
Automate workflows using prompt engineering in chatbots, knowledge assistants, and content generation.
Create multilingual prompts to support diverse audiences and contexts. Hands-On Mastery
Experiment with iterative prompt testing to achieve optimal results.
Build mini projects (e.g., AI tutor, compliance checker, creative writing assistant).
Document and present prompt strategies in a structured, reusable format for teams or organizations.
