Become an AI Engineer
For developers building real features on top of LLMs.
From prompting to retrieval to agents to evaluation and serving — the full arc of shipping LLM-powered features, threaded through handbooks, system designs, runnable challenges and tools.
- Structure prompts and LLM calls that hold up in production
- Build a retrieval pipeline from chunking to ranked context
- Design agent + tool-calling systems and evaluate them honestly
- Reason about inference cost, latency and serving at scale
The Prompting Handbook
Start at the interface to every model — prompting.
Attention Is All You Need
See what you’re actually prompting — the Transformer, explained.
Context Budget & Cost Planner
Feel how context length drives latency and cost.
Design a RAG Pipeline
The canonical RAG architecture, end to end.
Design a Vector Database
Go inside the vector store: IVF, HNSW, ANN search.
Cosine Similarity
Implement the similarity metric retrieval runs on.
Top-K Retrieval
Rank documents by similarity — the “R” in RAG.
RAG Chunking Playground
Tune chunking and watch retrieval quality shift.
Design Semantic Search
Assemble it all: embeddings, hybrid retrieval, reranking.
Design an AI Agent System
Move from single calls to tool-using agents.
Design Multi-Agent Orchestration
Coordinate many agents with a durable workflow engine.
Tool-Schema Designer
Design the tool interfaces agents call.
The Agent Evaluations Handbook
Evaluate agents — the hard part of shipping them.
Token-Level F1
Implement a real eval metric by hand.
The Loop Engineering Handbook
Then stop prompting — design the loops that run your agents unattended.
Design an LLM Inference Server
Serve models at scale: batching, KV-cache.
The Senior AI Engineer Interview Handbook
Tie it together at senior scope.
What this path builds toward
AI engineering isn't one skill — it's a stack. This path walks it in the order the work actually happens: start at the prompt, the interface to every model; add retrieval so the model answers from real data; move to agents that take actions through tools; then the two things that separate a demo from a product — honest evaluation and cost-aware serving at scale.
Each step is a concrete artifact — a system design you can reason about, a tool you can play with, a metric you implement by hand — not a video to watch. By the end you'll be able to take an idea from "call an LLM" to a grounded, agentic, evaluated feature you'd be comfortable shipping.