AIDU-FM-103
Delivery Type: Live, instructor-led Remote or In person
Prerequisites: AI Foundations for Professionals, Machine Learning for Professionals
This course provides professionals with a clear, non-technical understanding of foundation models, large language models, and multimodal AI as they operate in real organizational settings. It explains how these systems differ from traditional machine learning, why fluent outputs do not imply understanding or truth, and how multimodal systems combine text, images, audio, and other signals.
Rather than teaching tools, prompting tricks, or vendor-specific workflows, the course builds durable mental models for how these systems actually behave. Participants examine common failure modes such as hallucinations, prompt sensitivity, over-generalization, and misuse, and learn why naive adoption creates operational, legal, and reputational risk.
The course connects model behavior to governance, accountability, safety, and evaluation, enabling participants to determine where foundation models add value and where they should not be relied upon. Special attention is given to evaluating vendor claims, benchmarks, and demonstrations versus real-world performance.
Core Topics:
What foundation models are and how they differ from task-specific ML
How large language models generate outputs without understanding meaning
Emergent capabilities and their limits
Prompting as interface, not control
Fine-tuning and adaptation tradeoffs
Multimodal models and integrated representations
Hallucinations and confident failure
Evaluation challenges for foundation models
Safety, risk, and misuse at scale
Human-in-the-loop oversight and accountability
Organizational adoption patterns
Vendor claims versus operational reality
Outcomes:
Explain what foundation models are and how they differ from traditional ML
Understand how LLMs generate fluent but unreliable outputs
Describe how multimodal AI integrates text, images, audio, and other signals
Identify common failure modes including hallucinations and prompt instability
Distinguish prompting, fine-tuning, and system-level controls
Evaluate safety, compliance, data leakage, and misuse risks
Set appropriate organizational boundaries for deployment
Critically assess vendor claims, demos, and benchmarks
