Learn the architecture behind reliable AI products: RAG, agents, evalsand the systems that keep them running in production.
Five topics, in the order production problems show up. Each one runs the same loop: read it, see it, animate it, test yourself, then build it.
Chunking, hybrid retrieval and reranking, and the evals that tell you retrieval is the part that is failing.
Start with RAGRead pricing and benchmarks critically, route by task, and know what a request really costs before it ships.
Compare modelsGuardrails on input and output, tracing on every tool call, and fallbacks for when a provider or a plan fails.
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Azure OpenAI + AI Search + embeddings — real-world architecture for production AI systems, including legacy data, orchestration, hybrid retrieval, cost control, and failure modes.
The AI domains sit at the top of the map. The engineering foundations around them (system design, APIs, cloud, data, testing) are the parts an AI system still depends on. 17 domains, every topic in the same five modes.
Learning isn't linear. Cycle through all five cognitive modes on every topic — from first encounter to shipped code. Complete the loop, earn XP.
The four Claude tiers, adaptive thinking and effort, the tool-use rules that differ from other providers, prompt caching, and where you can run it.
The GPT-6 tiers, what reasoning effort replaced, when to use the Responses API over Chat Completions, how the bill is calculated, and what is different on Azure.
Persist agent knowledge beyond the context window — vector stores, key-value caches, and structured stores for cross-session recall.
Production-time evaluation — implicit user feedback, A/B testing, and real-time LLM-as-judge scoring for continuous quality monitoring after ship.
How to build an AI code review system that catches real issues — architecture, prompt design, GitHub integration, and practical lessons.
Monitor token usage and costs — per-request tracking, budgets, cost allocation across teams, and the optimization strategies that shrink the bill.
Don't just read about it. Explore fully operational sandbox environments and modular tools to understand how the architecture handles real-world complexity.
Visual explainers dissecting how technology works under the hood.
Execution visualizer parsing Python logic in real-time motion.
Count tokens and estimate context usage before you send.
Model the monthly bill for a given volume and model mix.
Watch chunking, embedding, retrieval and reranking run.
Iterate on a prompt against a fixed set of cases.
