AI and Large-Language-Model Foundations

Distinguish deterministic software, predictive machine learning, generative AI, and agentic systems by their behavior and risks.

Map the lifecycle of an AI capability from data and experimentation through evaluation, deployment, and monitoring.

Explain tokens, context windows, embeddings, attention, and next-token generation at an architecture-review level.

Identify hallucination, context, bias, privacy, security, latency, and cost failure modes in an LLM feature.

Define offline and online evaluation criteria for an LLM-powered user journey.

Estimate the principal latency and cost drivers of an LLM application.