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AI & ML Engineering

LLMs · RAG · Agents · MLOps · 7 Technologies

From AI fundamentals to production LLM systems. Master the complete AI engineering stack — LangChain, RAG, Agents, LLMOps, and beyond.

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AI Foundations

Core concepts every AI engineer must know

ML concepts, neural networks, model types — from zero to AI-ready

Zero-shot, few-shot, CoT, ReAct, system prompts — master LLM communication

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LLM Application Development

Build production LLM-powered applications

LLM framework — chains, LCEL, memory, tools, structured output

Retrieval Augmented Generation — chunking, embeddings, vector DBs, evaluation

Autonomous agents — ReAct, tool use, multi-agent, memory systems, production

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MLOps & Production AI

Deploy, monitor, and maintain AI systems

Production LLM ops — prompt versioning, caching, evaluation, guardrails, cost

GPT-4, DALL-E, Whisper, embeddings — building with OpenAI's platform