Use this section to build the technical vocabulary and production judgment expected in Forward Deployed Engineer interviews.
The path is intentionally split in two:
- Foundations: how LLM applications are assembled.
- Production: how they survive real users, real data, latency, cost, safety, and rollout pressure.
How the lesson pages work
Every lesson uses the same devices. A Before reading on prompt asks you to commit to an answer first — attempt it before reading on, because a wrong guess makes the explanation stick. Bad / Good / Great examples grade real answers, and the payload is the stated reason why Great beats Good. Core boxes hold the one thing to remember; Deeper notes hold nuance you can skip on a first pass and return to.
Recommended Order
- LLM App Building Blocks - the basic parts of an AI application.
- Prompting & Structured Output - how to make model behavior legible and useful.
- Retrieval (RAG) & Vector Search - how systems ground answers in external knowledge.
- Tool Use & Function Calling - how models take actions.
- Agents & Orchestration - when a workflow becomes multi-step.
- Model Context Protocol (MCP) - how tools and context connect to models.
- Evaluations - how you know the system works.
- Guardrails & Safety - how you bound failures.
- Observability & Debugging - how you find and explain failures.
- Cost & Latency - how you make the system viable.
- Security, Compliance & Data - how you keep the deployment trustworthy.
- Deployment & MLOps - how you ship and operate it.
Best Next Step
Start with LLM App Building Blocks if you are new to AI systems.
Jump to Evaluations if you already know the basics and want the highest-leverage production topic.
Next: LLM App Building Blocks — start at the first block.
How production AI fits the FDE job
A Forward Deployed Engineer is rarely hired to invent a foundation model. The job is to turn a general capability into a dependable system inside one customer's data, permissions, workflows, latency budget, security rules, and definition of success. That makes the connective tissue between the lessons more important than any single framework.
The learning sequence follows the deployment: assemble context and tools, decide whether orchestration is justified, define evaluations before launch, bound failures with guardrails, instrument the system, control cost and latency, satisfy security and compliance, and ship through the customer's operational process. In an interview, naming those connections is stronger than reciting definitions.
What interview-ready understanding looks like
- You can sketch the simplest useful architecture and explain why a more autonomous design is not yet earned.
- You define quality with a representative evaluation set rather than saying the model seems accurate.
- You identify the dominant customer constraint—privacy, latency, cost, adoption, or risk—before choosing components.
- You can describe how the system is observed, rolled back, handed over, and improved after launch.