Advanced Generative AI for Business

This unit advances learners from foundational GenAI concepts to applied, business-focused techniques: advanced generative model architectures, customization of LLMs, experiment design, and iterative content production for marketing and operations. Learners will synthesize technical trade-offs with practical tasks (SEO, social media, branding, and content repurposing), evaluate risks and quality, and produce a mini GenAI solution for a business use case.

Learning Objectives

• Analyze advanced generative model architectures (VAE, flows, WGAN, autoregressive and hybrid approaches) by comparing strengths, limitations, and suitability for at least three distinct business applications and summarizing findings in a short technical report.
• Apply model customization and prompt engineering techniques (feature engineering, prompt sequencing, fine-tuning/adaptation) to produce and iteratively refine three business-oriented artifacts (e.g., SEO-optimized article, social media campaign content, and logo/slogan concept), documenting versions and rationale.
• Evaluate generated content for coherence, factual accuracy, legal/IP and ethical risks, and implement mitigation strategies; produce a risk assessment and a revision log documenting at least one measurable quality improvement per artifact.
• Demonstrate the ability to design and execute a mini GenAI project: define objectives, select or adapt an appropriate model pipeline, run experiments, analyze results (including basic SEO/engagement metrics), and present a final deliverable with actionable recommendations.