AI made simple: RAG, agents and evals, one clear step at a time,with visuals and hands-on builds that anyone can follow.
179articles
18domains
27cert mock exams
1,353practice questions
Five topics, in the order real projects run into them. Start at RAG and work along.
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.
Add guardrails27 mock exams and 1,353 practice questions across 7 vendors, each answer explained with a diagram. Score 80% or more on a full exam to earn a Silver, Gold or Platinum certificate badge. Every exam is free right now, including the Pro ones.
Microsoft
Amazon Web Services
Google Cloud
NVIDIA
Databricks
Azure OpenAI + AI Search + embeddings — real-world architecture for production AI systems, including legacy data, orchestration, hybrid retrieval, cost control, and failure modes.
The most-read articles of the last seven days, refreshed every hour.
How MCP works, how to build MCP servers that expose tools to AI agents, and practical patterns for connecting LLMs to your systems.
The Thought → Action → Observation loop that lets LLMs use tools, verify intermediate steps, and self-correct — the pattern behind most modern AI agents.
A practical walkthrough of evolving a basic LLM chatbot into a capable agent — adding tool calling, persistent memory, and multi-step planning.
Agents fail with healthy infrastructure. One trace per run, OpenTelemetry GenAI spans on a schema that is still moving, cost per task, and evals that catch what dashboards miss.
JSON mode, tool schemas, parallel tool calls, and the architecture patterns that let LLMs interact reliably with external APIs and databases.
Pinecone serverless GA (2024), namespaces for multitenancy, sparse-dense hybrid, .NET PineconeClient, batch upserts, metadata filtering, built-in rerankers (BGE/Cohere), tenant offboarding via DeleteAll.
The newest updates, straight from the editor.
Prompt chaining, routing, orchestrator-workers, ReAct, plan-and-execute, reflection and tree search: what each costs, what the research shows, and when to climb to the next one.
Chunking strategies, embedding pipelines, retrieval patterns, and when RAG breaks down in production systems.
Getting code from an agent is easy. Getting code you can trust takes a way of working: explore, plan, implement, verify and review, with small diffs and a spec first.
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. 18 domains, every topic in the same five modes.
Cloud (Azure)Azure services, microservices, serverless, Service Bus, AKS, App Service — how real systems run in production on Microsoft cloud.6/6Infrastructure as CodeTerraform, Bicep, Pulumi — define, version and automate cloud infrastructure. Covers drift detection, policy as code and secrets management.10/10
TypeScript Deep DiveGenerics, utility types, conditional types, mapped types, Zod, type guards and module resolution — mastering the TypeScript type system.12/12
Backend (.NET / C#)C# language features, OOP, Gang-of-Four patterns, Clean Architecture, CQRS, DDD, Entity Framework and Minimal APIs — the enterprise backend spine.9/9
Frontend (React)Hooks, Fiber, Server Components, state management, React Query and Next.js App Router — the modern frontend runtime.6/6DatabasesPostgreSQL, Redis, Cosmos DB, pgvector, Pinecone — relational, cache, NoSQL and vector storage with production-grade patterns.12/12Read it, see it, play with it, test it, build it. Five ways into every topic, so pick the one that clicks and finish the loop for XP.
Don't just read about it. Poke at real tools, change the inputs and watch the systems work.
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.
It's the first step of the AI path, and the one most real projects need first.
