AI & LLM Engineering
Building real things with models — the differentiator for these roles.
LLM Foundations
What the model actually does, at exactly the depth an application engineer is expected to hold.
8 topics
Working with the API
The request/response surface you actually integrate against, and the four things that decide its cost and latency.
8 topics
Prompting & Context Engineering
Deciding what goes into the context window, in what order, and how you keep that decision from rotting.
7 topics
Tool Use & Function Calling
How a model reaches outside its own context, and why the tool surface is the part you actually design.
6 topics
Agents
When to build an agent, how the loop works, and how to keep it from going off the rails.
7 topics
Model Context Protocol
The standard for plugging tools and data into a model, and when a plain HTTP call is still the better answer.
5 topics
RAG & Retrieval
Getting the right few thousand tokens in front of the model, which is most of what makes a knowledge product work.
7 topics
Evals & Quality
The part that separates a demo from a product — knowing whether a change made the thing better.
6 topics
Observability & Cost
Seeing what your LLM feature actually did, what it cost, and how slow it was for the person waiting.
5 topics
AI Security
The threat model that arrives with a model — untrusted text becoming instructions, and everything downstream of that.
7 topics
AI Product Thinking
The judgement half of the interview — what to build with a model, and what not to.
5 topics