RAG Knowledge Check
Chunking strategies, embedding pipelines, retrieval patterns, and when RAG breaks down in production systems.
Test: RAG Knowledge Check
Scenario questions with explanations that show exactly where your model of it breaks.
Reading Retrieval-Augmented Generation is free. Pro unlocks the other four modes on all AI Engineering topics, so you can go from understanding it to building it.
- SeeThe architecture as diagrams, so you can hold the whole system in your head.
- AnimateStep through the flow one stage at a time and watch the data move.
- TestScenario questions with explanations that show exactly where your model of it breaks.
- BuildA guided implementation with code you can run and adapt for production.
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All AI Engineering articles →Guardrails: Input/Output Safety in Production
Input, output, schema, tool, and cost guardrails — the layered defence system that catches LLM failures before they reach a user or downstream system.
Embeddings: From Theory to Production Choice
Convert text, images, and code into dense vectors that capture semantic meaning — the foundational primitive of modern AI systems, and the choice that dominates RAG quality.
How to Build a Production-Ready AI System (Azure OpenAI + AI Search — Real Architecture)
Azure OpenAI + AI Search + embeddings — real-world architecture for production AI systems, including legacy data, orchestration, hybrid retrieval, cost control, and failure modes.
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