AI Mastery
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Foundations → Build → Operate → Optimize, model-agnostic, eval-driven
The Academy already contains AI courses; this pathway turns them into one end-to-end mastery route. The production benchmark emphasizes system scoping, grounding, tools/agents, evaluation, reliability, security, latency/cost and measurable task success — not prompt tricks alone.
Benchmark: OpenAI Academy + Microsoft Learn AI Agent curricula (current Sep 2026)
Mastery Ladder
UNDERSTAND
LLM behavior, tokens/context, prompting and context engineering, structured outputs, embeddings, model selection, limitations and responsible use.
GROUND + ACT
RAG and retrieval quality, tool/function calling, agents, MCP, memory/state, workflow orchestration, human handoffs and product integration.
EVALUATE + RELY
Golden datasets, graders, regression gates, traces, prompt/model versioning, observability, prompt injection defenses, permissions, latency and cost.
Foundations & Build — Core Video Bridges
Production Bootcamp — System Scoping, Evals, Reliability, Safety, Cost, Adoption
Scope the system
Evaluation system
Reliable agents
Build effective agents
7-Day AI Builder Bootcamp
Day 1 – Scope: pick a costly real workflow; define user, task, baseline, non-goals, risks and measurable success. Day 2 – Ground: build retrieval with source metadata and a small relevance test set; inspect failure cases. Day 3 – Act: add one tool/API/MCP integration with explicit permissions, timeout, validation and fallback. Day 4 – Evaluate: create representative cases, rubrics/graders and regression thresholds for quality and safety. Day 5 – Operate: instrument traces, latency, token/cost and tool failures; add retries only where evidence supports them. Day 6 – Red Team: test injection, unsafe tool requests, data leakage, ambiguity and over-authority; tighten boundaries. Day 7 – Ship: stage rollout, human escalation, rollback/runbook, executive demo and measured adoption/task success.
AI Mastery Capstone
Ship a production-style AI workflow with grounded context, at least one safe tool integration, an inspectable eval set, regression gates, traces, latency/cost metrics, explicit security/safety controls and a runbook. Defend when you would use prompt/context changes, RAG, fine-tuning, deterministic code or no AI at all.