Editorial desk with abstract AI data visualizations and research notes

AI Learning Ramp

Course index for frontier AI systems prep.

A three-times-a-week ramp from GenAI query systems into AI infrastructure, agentic systems, evals, safety, and OpenAI or Anthropic style interviews.

Plan

Each new course page should fit one focused hour: a short reading set, a system-design frame, and one interview drill tied back to your BigQuery GenAI query-engine background. The first pass prioritizes AI engineering interview fundamentals before agent breadth.

Published Courses

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Upcoming Roadmap

The automation will add new course pages here as they are published.

Course 2: Serving Engine TradeoffsvLLM, TGI, TensorRT-LLM, SGLang, routing, SLOs, and capacity planning.
Course 3: Latency And Cost ModelsTTFT, ITL, throughput, admission control, prompt caching, and cost-per-token.
Course 4: Retrieval SystemsChunking, embeddings, hybrid search, reranking, freshness, and provenance.
Course 5: Context EngineeringContext selection, compression, prompt assembly, caching, and long-context failure modes.
Course 6: Text-To-SQL SystemsSchema linking, semantic layers, dry runs, query repair, and permission safety.
Course 7: Agent PatternsWorkflows, agents, router, evaluator-optimizer, orchestrator-worker, and handoffs.
Course 8: Tool Use And MCPFunction calling, tool schemas, auth, sandboxing, retries, and Model Context Protocol.
Course 9: Durable Agent ExecutionState machines, queues, checkpoints, human approval, replay, and cancellation.
Course 10: Eval-Driven DevelopmentGolden sets, LLM judges, human review, regressions, and release gates.
Course 11: ObservabilityTraces, tool receipts, state diffs, latency metrics, and failure replay.
Course 12: Production ReliabilityFallbacks, backpressure, rate limits, model drift, rollout strategy, and incident loops.
Courses 13-24Scaling, data/query intelligence, safety, enterprise controls, and interview mock designs.