01
Core AI engineering
Models, prompts, and retrieval — the vocabulary every loop opens with.
- LLM fundamentals, integration, and prompt design
- RAG pipelines, embeddings, and semantic search
- Evaluation, guardrails, and structured output
Gen AI · Fundamentals to production engineering
GenAIFundamental
Structured Gen AI engineering — LLMs, RAG, agents, and production systems in one curriculum.
Gen AI loops test integration, retrieval, and trade-offs — not tokenizer trivia alone.
Build with models, defend your design in the room — free from fundamentals to capstone.
Free guides · Pair with machine coding practice
What you'll master
Foundations, production systems, and interview defense — one structured Gen AI track.
02
Production AI systems
Agents, streaming UX, and backends you can defend under pressure.
- Multi-step agents and tool-calling workflows
- Copilots, streaming chat, and AI-powered UI
- SSE, queues, observability, and system design
03
Interview-ready outcomes
Trade-offs, debugging drills, and a capstone that ties the track together.
- Architecture patterns and production trade-offs
- Performance, security, and whiteboard scenarios
- Full-stack AI mini project capstone
Explore topics
The full syllabus — click any guide to dive in.
| # | Topic | You'll explore |
|---|---|---|
| 1 | LLM Fundamentals Tokens, transformers, embeddings, context | Tokens, transformers, embeddings, context |
| 2 | LLM Integration APIs, streaming, tool calling, structured output | APIs, streaming, tool calling, structured output |
| 3 | Prompt Engineering CoT, ReAct, guardrails, evaluation | CoT, ReAct, guardrails, evaluation |
| 4 | RAG Pipelines Chunking, retrieval, reranking, hybrid search | Chunking, retrieval, reranking, hybrid search |
| 5 | Vector Embeddings ANN search, vector DBs, similarity | ANN search, vector DBs, similarity |
| 6 | Semantic Search BM25 + vectors, ranking, optimization | BM25 + vectors, ranking, optimization |
| 7 | AI Agents & Workflows Planning, tools, multi-step agents | Planning, tools, multi-step agents |
| 8 | AI-powered UI Systems Copilots, streaming chat, AI UX | Copilots, streaming chat, AI UX |
| 9 | AI Backend Engineering SSE, queues, caching, observability | SSE, queues, caching, observability |
| 10 | AI System Design Enterprise RAG, scalable AI architecture | Enterprise RAG, scalable AI architecture |
| 11 | AI Performance & Security Latency, eval, prompt injection | Latency, eval, prompt injection |
| 12 | AI Interview Scenarios Debugging, tradeoffs, whiteboard drills | Debugging, tradeoffs, whiteboard drills |
| 13 | Full-stack AI Mini Project Full-stack support copilot capstone | Full-stack support copilot capstone |
Built on InterviewPro
Ship org-knowledge AI
You learn agents and production AI in this track—then explore DoxPro, InterviewPro’s multi-tenant RAG product.
Real Gen AI project
DoxPro
Org admins create a workspace, add knowledge docs, get a public key, and embed a RAG-powered AI assistant in their product within minutes. Hybrid retrieval, source-cited answers, SSE streaming, and refusal guardrails when docs do not match.
- Implements Gen AI topics end-to-end in a real product
- Hybrid search: embeddings + keyword retrieval
- Cited source chips for grounded support replies
- SSE streaming chat UX with workspace branding
- Public key embed · secret key console