// LOADING
Six modules. Forty-three lessons. Each grounded in production reality, not textbook theory.
Every flavor of RAG, from the textbook chunk-embed-retrieve loop to GraphRAG, agentic RAG, and CRAG. With the trade-offs and the production failure modes.
Vector spaces, model trade-offs, chunking strategies, hybrid search. Including multilingual Arabic/English realities most courses skip.
Choosing models like a senior engineer: by task fit, cost curve, latency, compliance, and how to keep up as the leaderboard shifts monthly.
ReAct, LangGraph state machines, multi-agent patterns, memory architectures, and the realities of running agents at production scale.
Observability, evals, guardrails, latency, cost, CI/CD, and ANN at scale. Everything that turns a notebook into a system.
Multi-tenant RAG, legacy integration, Azure OpenAI, Responsible AI, and how to build the business case. The reality of shipping AI inside large regulated organizations.