|
Hi {{ name }}, A student asked me last month: “As an AI engineer, do we just build things like ChatGPT for companies?” Fair question. Companies already have the models. What they need is someone who can make a model work on their data, follow their rules, and keep working on a busy Monday morning. That work usually looks like one of these five. |
|
01 |
Assistants that answer from company documents
An HR assistant that answers from 400 policy PDFs and shows the page it used. RAG · Module 07, the biggest in the course
|
02 |
Agents that take actions
An expense agent that reads a receipt, checks the policy and files the claim. Agents & LangGraph · Modules 06 and 10
|
03 |
Connectors that plug AI into everything
Your own MCP server that lets any AI assistant query your database safely. MCP, built from scratch · Module 08
|
04 |
Teams of agents
A researcher, a writer and a reviewer agent that hand work to each other. Multi-agent orchestration · Module 12
|
05 |
The quality and safety layer
Evals that catch wrong answers before users do, guardrails that block prompt injection, dashboards that track cost. Evaluation, guardrails, monitoring · Modules 11, 13, 15
|
|
That fifth one is what most tutorials skip, and it’s what interviewers ask about most. Getting a demo working is easy. The engineer who can measure why it fails and fix it is the one who gets hired. In the course you build all five as one product that grows module by module: 15 module projects that build on each other, then 5 capstones. Want to see what a lesson looks like? Here’s a real coding lesson from inside the course. |
|
BC |
Balaji Chippada
AGENTIC AI ENGINEER & EDUCATOR · 9+ YRS PRODUCTION AI |
P.S. I build everything from scratch first (a mini LangChain, hand-written tools, an MCP server) before using frameworks. That’s why students can explain their projects in interviews. | |
Build it. Don’t just watch it.
|
|