Engineering Series · Course 1 of 6
// Stop prompting. Start engineering.

AI Engineering Foundations.
The production layer nobody teaches.

LLM API architecture. Prompt architecture (not "prompt engineering"). Tool use via MCP. The GRAIL Loop in code. Evaluation-first development. Your first governed AI service.

29
lessons
6
modules
6–8
weeks self-paced
Get on the waitlist
€197 waitlist
Self-paced · Full code repo (Apache 2.0)
The gap

The demo works.
Production doesn't.

Every demo works. That is the easy part. You build an agent in an afternoon. It handles the happy path. The stakeholders clap. Someone says "ship it."

Then Monday arrives. The model costs 4x what you budgeted. The latency is unacceptable for the actual user flow. The agent hallucinates on edge cases nobody tested. There's no logging. No audit trail. No way to know which version of the prompt produced which output.

Security asks: where does the data go? Legal asks: who's liable? Finance asks: what's the unit cost per request? Ops asks: how do we monitor this?

No agent framework answers these questions. Because these are not prompting problems. These are engineering problems.

The model is a commodity. The architecture around it is the asset.

This course teaches the engineering layer that separates demos from deployed systems: API architecture, prompt architecture, tool use via MCP, evaluation pipelines, and governance you can defend in a security review.

The method

29 lessons. 6 modules.
Every one production-specific.

Not a chatbot tutorial. Every module addresses the engineering layer that survives model swaps, vendor changes, and compliance audits.

Module 1 · Primer 01
"You're not writing code. You're designing behavior."

The Paradigm Shift

Four generations: Chatbot → RAG → Agent → Platform. The seven components every agentic platform requires. The Three V's (Variability, Veracity, Vulnerability). Why "good code" is not "good AI system." The ADLC replaces ship-and-hope.

5 lessons
Module 2 02
"Beyond the API call."

LLM APIs: The Engineering Layer

OpenAI, Anthropic, open-weight (Qwen, Llama, Mistral) with honest trade-offs. Structured outputs, function calling, tool schemas. Streaming, token management, cost. Error handling for probabilistic systems.

Lab: Multi-model abstraction layer with automatic fallback
5 lessons
Module 3 03
"System prompts are contracts, not incantations."

Prompt Architecture

System prompts as architectural contracts. The 5-part prompt structure: role, context, task, output schema, constraints. Context assembly from governed sources. Versioning, testing, regression detection.

Lab: Prompt management system with versioning and A/B testing
5 lessons
Module 4 04
"The hands of the system."

Tool Use and MCP

The Model Context Protocol (MCP), the standard the industry is converging on. Building MCP servers that expose your APIs safely. Input validation, output sanitization, schema enforcement. Tool permissions and least-privilege.

Lab: 3 MCP tool servers (database, file system, external API)
4 lessons
Module 5 05
"The quality engine, in code."

Evaluation and the GRAIL Loop

Generate → Rank → Aggregate → Iterate → Launch, implemented in code. Self-consistency: running the same query N times and analyzing agreement. Test suites for probabilistic outputs. Deterministic checks. PASS / REVIEW / INCONCLUSIVE verdicts.

Lab: Complete evaluation pipeline with structured verdicts
5 lessons
Module 6 · Capstone 06
"Not a chatbot. A governed system."

The Governed Document Q&A System

Architecture overview. Prompt architecture plus multi-model. MCP tools plus document retrieval. GRAIL evaluation pipeline. Structured logging and basic cost tracking. A governed system that retrieves, reasons, verifies, and logs.

5 lessons + capstone
The capstone arc · across the series

One project. Six courses. Six layers.

Your E1 capstone is not a throwaway. It evolves through E2 to E6 into a deployed Enterprise AI Operating System. One project. One portfolio piece. Six layers of production engineering.

E1 · Now
Foundation
Versioned prompts. Multi-model. MCP. GRAIL eval. Logging.
E2
+ Trust
Self-consistency. TrustGate. Reliability guarantees. Drift detection.
E3
+ Governance
7 services. Platform Protocol. ~15 APIs.
E4
+ Security
Guardrails. Agent Auth. Sandboxing. Red-team tested.
E5
+ Context
Multi-source. RAG. Context Router. RBAC.
E6
Full Platform
All 4 layers. Org Agents. Intelligence. Desktop Shell.
Prerequisites & tech stack

What you need. What you'll use.

Prerequisites

Comfortable with Python. Basic understanding of APIs and web services. No ML or data science background required: this is engineering, not research. If you can write a Flask or FastAPI app, you're ready.

Tech stack

Python 3.12, FastAPI, Docker, OpenAI and Anthropic APIs, open-weight models via LiteLLM, MCP protocol. All code is yours: Apache 2.0 licensed open-source repos.

Honestly

This is for you if:

→You're a software engineer, ML engineer, or technical lead
→You've built AI demos and need to take them to production
→You need to answer enterprise questions: security, cost, audit, testing
→You want the engineering foundations before trust, agents, security (E2-E6)
→You're starting the Engineering Series and want to build right from lesson one

Don't take this if:

→You've never written code. Start with the non-engineering courses.
→You want business-level AI skills. See the For Everyone catalog.
→You're already shipping production AI with full evaluation pipelines. Consider starting at E2.
Pricing

One price. Lifetime access.

€197
One-time payment. All future updates included.
  • 29 lessons across 6 modules (video, written, runnable code)
  • Multi-model abstraction layer and prompt management system
  • 3 MCP tool servers and the GRAIL evaluation pipeline
  • The E1 capstone: Governed Document Q&A System
  • Full code repository (Apache 2.0) and lifetime updates
3 months in the Engine Room. Where alumni and operators go to get unstuck.
Get on the waitlist

Want all six courses?

See the Engineering Series bundle →
FAQ

Before you ask.

Anything else? Email [email protected].

Is this prerequisite for the rest of the Engineering Series?
Yes. E1 establishes the production paradigm (APIs, prompt architecture, tool use via MCP, evaluation-first development) that E2–E6 build on. If you start anywhere else, you'll be missing the underpinnings.
What stack will I use?
Python 3.11+, an LLM API key (OpenAI, Anthropic, or both), Docker for the capstone, and MCP for tool use. The capstone is a Governed Document Q&A System you actually deploy.
I've built LLM apps before. What's different here?
Most LLM apps demo well and break in production. E1 is about the gap between the two: structured prompts (not prompt 'engineering'), evaluation-first instead of vibes-based testing, governance hooks from day one. The 'Prompt Architecture' module alone is worth the course if you've been writing prompts as strings.
Programming language?
Python primarily. The patterns translate to TypeScript / Go / Rust, but the lessons are Python. If you can read a function and a class, you'll be fine.
What does 'the demo works, production doesn't' actually mean?
It means your LLM prototype passes 5 example queries and fails on the 6th in a way that costs you. E1 is built around the engineering practices that close that gap: structured outputs, eval suites, the GRAIL Loop (Generate, Rank, Aggregate, Iterate, Launch), and observability.
Time commitment?
20–30 hours across 6 modules. Self-paced: about 6–8 weeks of evenings and weekends.
Can my company pay for this?
Yes. Invoices issued. Email [email protected] subject 'Reimbursement.'
What's the refund policy?
€197 courses are non-refundable once you open the course. Unopened, they are refundable in full within 14 days of purchase. The Engineering Series bundle (€797) does offer a 14-day conditional refund — most engineers buying multiple courses pick the bundle.

Stop prompting. Start engineering.

The production foundations that make everything else possible. 29 lessons. 6 modules. Your first governed AI system. €197.

Get on the waitlist
See the full series: The Engineering Series