The Agentic AI Academy is a structured path from zero to production-grade AI engineering — agents, RAG, LLMs, MCP, and everything in between. No paywalls. Ever.
A 4-agent system with Planner, Researcher, Writer & Reviewer powered by CrewAI and hybrid RAG search.
Production RAG with metadata filtering, hybrid search, contextual compression and RAGAS evaluation.
A published MCP server connecting your agent to external APIs — GitHub, Slack, or your own service.
A production-grade AI system on cloud with CI/CD, monitoring via Langfuse, and cost tracking.
Use a coding agent (Claude Code or Cursor) to autonomously build, test, and document a REST API from a spec.
A domain-specialized model using LoRA/QLoRA with evaluation benchmarks and hosted on HuggingFace.
Ask anything about AI agents, RAG, LLMs, prompt engineering, or the curriculum. Powered directly by the Claude API — your key stays in your browser.
The Agentic AI Academy is more than a course — it's an open community of builders. Share agents, publish RAG cookbooks, join hackathons, earn badges, and mentor the next generation of AI engineers.
Retrieval-Augmented Generation lets you give an LLM access to your own documents — without fine-tuning. Here's how it works, why it matters, and how to build your first pipeline in under 50 lines of code.
IntermediateBoth frameworks let you build multi-agent systems — but they take very different approaches. LangGraph gives you a stateful graph with fine-grained control; CrewAI gives you role-based teams. Here's when to use each.
AdvancedNaive RAG breaks in production. From HyDE and RAPTOR to Self-RAG and GraphRAG, here are the patterns that separate demo-quality retrieval from systems that actually hold up at scale.
ToolsAnthropic's Model Context Protocol is quietly becoming the USB-C of AI tooling — a standard way for agents to talk to any external service. Here's what it is, how it works, and how to build your first MCP server.
Free forever. No credit card. No paywalls. Just knowledge.