Exploring the limits of AI: generative AI, NLP, AGI, and future trends

This course breaks down the “limits of AI” in an actionable way: instead of debating AGI vaguely, you will understand exactly why current generative models still struggle with causal reasoning, up-to-date knowledge, and output consistency. At the same time, you’ll learn practical “patches” such as source-based RAG, function calling to access verification tools, schema-based prompts, and self-check mechanisms. Building on this, the content presents a standard architecture including the model layer, knowledge layer, tools layer, and workflow/agent layer, along with guardrails, logging, and evaluation processes to make AI truly operational in a business setting. The “what’s next” section guides you from demos to real products: setting task-based objectives, choosing models based on cost and latency, building human-in-the-loop workflows, and scaling in a controlled manner. By the end, you will have a clear framework to leverage the strengths of current AI without falling into familiar limitations.