Gen AI Fundamental
Build with models. Ship with confidence.
From LLM fundamentals to a production capstone. One structured curriculum—13 topics aligned with how Gen AI interviews are run today.
Free guides · Pair with machine coding practice
Who it's for
Built for Gen AI engineering roles
Whether you integrate LLMs, ship AI-powered UI, or own RAG and agents end to end—one curriculum aligned with how those interviews are run.
- LLM engineer
- Gen AI engineer
- AI frontend engineer
- Full-stack AI engineer
- RAG & retrieval engineer
- AI application engineer
Three pillars
What you'll master
From tokenizer basics to shipping agents—grouped the way Gen AI interviews actually run: foundations, production systems, then defense under pressure.
Core AI engineering
Models, prompts, and retrieval—the vocabulary every loop opens with
- LLM fundamentals — transformers, tokens, embeddings
- LLM integration — APIs, streaming, tool calling
- Prompt engineering — guardrails, evaluation
- RAG, embeddings & semantic search
Production AI systems
Agents, streaming UX, and backends you can defend under pressure
- AI agents & multi-step workflows
- AI-powered UI — copilots, streaming chat
- Backends — SSE, queues, observability
- System design & performance/security
Interview-ready outcomes
Trade-offs, debugging, and a capstone that ties the track together
- Architecture patterns & trade-offs
- Production debugging scenarios
- Coding walkthroughs & edge cases
- Full-stack AI mini project capstone
Every topic covered
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
Our AI agents
You learn agents and production AI in this track—then try the AI assistant, InterviewPro's RAG-powered support chat.
AI assistant
Built into InterviewPro as AI assistant and also showcased as Aidesk Support Copilot — hybrid retrieval, source-cited answers, SSE streaming, and refusal guardrails when docs do not match.
- Implements all 11 major Gen AI topics end-to-end
- Hybrid search: embeddings + BM25 reranking
- Cited source chips for grounded support replies
- SSE streaming chat UX with follow-up memory
- Injection checks + low-confidence refusal guardrail