GenAI Engineer

Prepare for GenAI system design interviews — RAG, agents, MCP, and more

The hottest interview topic in 2025-26. "Design a RAG pipeline" and "Design an AI agent system" are now standard system design questions at FAANG+ companies. This path covers RAG, AI agents, MCP, advanced prompting architectures, and the LLM fundamentals you need to ace GenAI system design rounds.

Software Engineers building AI featuresBackend Engineers integrating LLMsFull-Stack Engineers building AI productsSolutions Architects designing AI systems
2Free Modules
4Premium Modules
6Roadmap Steps

Your Learning Path

A step-by-step roadmap from foundations to mastery. Follow this sequence for the most effective learning experience.

Understand the GenAI engineer role and skills landscape
Master advanced prompting architectures (CoT, ReAct, prompt chaining)
Build a strong mental model of LLM internals (transformers, attention, tokenization)
Design and build production RAG pipelines with evaluation
Architect autonomous AI agents with tool use and memory
Implement MCP servers to connect AI models to real-world data and tools

Modules

6

2 free modules to get you started, plus 4 premium deep-dives.

1Free

GenAI Engineer Roadmap

The complete learning path for GenAI engineers: from LLM fundamentals to production RAG and agent systems. Understand the skills landscape, job market, and where to focus your learning.

15 minStart
2Free

Advanced Prompting

Go beyond basic prompting: chain-of-thought, tree-of-thought, ReAct, self-consistency, prompt chaining, meta-prompting, and building robust prompt architectures for production systems.

15 minStart
3Premium

LLM Foundations for Engineers

The transformer architecture explained for engineers (not researchers). Attention mechanisms, tokenization, inference pipeline, KV cache, context windows, and how understanding internals makes you a better GenAI engineer.

60 minUpgrade to access
4Premium

RAG — Retrieval-Augmented Generation

End-to-end RAG system design: document chunking strategies, embedding models, vector databases (Pinecone, Weaviate, pgvector), retrieval algorithms, re-ranking, hybrid search, evaluation metrics, and production deployment patterns.

60 minUpgrade to access
5Premium

AI Agents

Design and build autonomous AI agents: ReAct pattern, tool use, planning and reflection loops, multi-agent orchestration, memory systems, guardrails, and frameworks (LangGraph, CrewAI, Autogen).

60 minUpgrade to access
6Premium

Model Context Protocol (MCP)

Anthropic's open standard for connecting AI models to external tools and data. Build MCP servers, understand the protocol spec, integrate with Claude Desktop and Claude Code, and design tool schemas for production use.

45 minUpgrade to access

Start Free — No Account Required

These foundational resources are free for everyone. Build your AI literacy before diving into persona-specific modules.

Prompt 101GenAI GlossaryBest Free ResourcesCertificationsUse Cases

Unlock All 4 Premium Modules

Get full access to every GenAI Engineer module — plus all other GenAI personas, DSA content, and System Design content with a single subscription.

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