AI For Builders

What this course is

This course is about learning how AI systems work, well enough to build with them. You leave with a working, deployed AI prototype and a repeatable framework, not a chat transcript.

Who should attend

Technical operators and builders moving from AI users to AI implementers
Employees at mid-to-large companies tasked with building AI-enabled tools
Entrepreneurs building AI-powered products end to end

Why this stands apart

No hand-holding on tools

Platform-agnostic. Real developer tooling and cloud platforms, not one prescribed stack.

Full builder framework

Model behavior, skills, MCP, fine-tuning against RAG, deployment, and evaluation.

A real capstone

A deployed prototype, presented to a nontechnical stakeholder.

Built-in evaluation method

Test and prove a system works, not just ship it and hope.

The agenda

Four Days,
Four Working Builds

DAY 1 · TUE OCT 27

Foundations and interaction

How these models behave, and how to get predictable, structured work out of them.

LLM fundamentals: what the model is doing and where it breaks
Model behavior, context, cost, and choosing the right model for a job
Skills: packaging capability the model can reliably invoke
Structured output and making results machine-usable

DAY 2 · WED OCT 28

Data, retrieval, fine-tuning, and deployment

The part everyone skips: getting real data in, deciding between fine-tuning and retrieval, and getting the thing off your laptop.

Data quality, and why most AI projects fail here first
Chunking and embeddings, with the tradeoffs made explicit
Retrieval-augmented generation on your own documents
Fine-tune a model in class, then compare it to your RAG system
Git and deployment basics: shipping something other people can reach
Local deployment and open source models: when to keep it in house

DAY 3 · THU OCT 29

Integration and architectures

Connecting a model to the systems you already run, and the architecture patterns that hold up once people depend on it.

MCP tool calling: connecting a model to systems you already run
Build chatbots several different ways, and weigh what each approach costs
System architecture patterns that hold up, and the tooling to get there
A brief look at agents and A2A handoff: what they are, and when to use them

DAY 4 · FRI OCT 30

Building, evaluation, and capstone

Prove it works, then present to someone who does not care how it works.

Implementation levels: how far to build before you validate
Evaluation: testing an AI system instead of trusting a demo
Quality control and what to monitor once it is live
Capstone: present your deployed prototype to a nontechnical stakeholder

Your instructors

Marcus Blair

Founder, Omega Digital Solutions

Marcus founded Omega to build AI systems that survive contact with a real business. He teaches the architecture sessions and runs the capstone reviews, including the part where your prototype gets questioned.

John McCulley

Founder, Mack2 Strategy

John works with leadership teams on strategy and operating decisions. He teaches the sessions on business value and on presenting technical work to the people who fund it.

John Derrick

Founder, Authentrics AI

John builds AI systems and thinks hard about whether they can be trusted. He teaches data quality, retrieval, and the evaluation work that separates a working system from a good demo.

Ryan McDaniel

Director of Operations, Cuantico

Ryan runs operations at Cuantico and has shipped AI into real processes with real constraints. He teaches deployment, tool calling and integration, and what goes wrong once other people depend on it.

Reserve a seat

Bring a laptop and something you want to build

Seats are limited to keep the room a real working session, so we talk to everyone before confirming. Send this and one of us replies within two business days with next steps and payment details.

$999 per seat through October 10, 2026. Regular price $1,799.
Prefer email? Write marcus@omegadigital.us directly.
Not the technical track? AI For Business runs four Thursdays in November.