LLM Foundations
Transformer-era language modeling concepts, architectures, and capability framing.
Llms And Agents
TRACKS
LLM Training, Alignment, and Serving
Training data, distributed pretraining, post-training, alignment loops, inference optimization, and production serving for large language models.
RAG, Agents, and LLM Production
Evidence-grounded LLM product architecture: RAG pipelines, agent-facing context, evaluation slices, observability, cost, safety boundaries, and production release judgment.
Reasoning and Frontier Post-Training
Synthetic data, verifiers, process supervision, reasoning traces, and frontier post-training loops.
Agent Runtime and Tool-Use Systems
Tool schemas, planners, sandboxes, browser agents, orchestration loops, and runtime failure handling.
Agent Memory, Planning, and Context Engineering
Design memory, context assembly, and planning state for long-lived LLM agents that must preserve evidence, recover work, and act under operational constraints.
Agent Safety, Guardrails, and Policy Systems
Guardrails, policy enforcement, action filtering, runtime controls, and the trust boundaries needed around agentic systems.
LLM Evaluation and Benchmarking
Offline evals, task suites, judge systems, reliability trade-offs, and the measurement discipline required to compare LLM behavior honestly.
Multi-Agent Systems and Coordination
Coordination protocols, role assignment, negotiation, and the design patterns for systems composed of multiple autonomous agents.
Tool Learning and Environment Interaction
Tool selection, environment feedback, learned interaction policies, and the mechanisms that let agents improve through action.
Human-AI Collaboration and Agentic Workflows
Design reliable work with AI assistants and agents: delegation, context, review, tool boundaries, memory, failure recovery, and human judgment.