AI Practitioner

3-Day Course: Pragmatic AI Systems Engineering

Designed for technical generalists ready to move past ChatGPT and start building structured, production-grade AI systems. Delivered as one day per week over three weeks — allowing time to digest and practice between sessions.

Day 1

AI Foundations & Your Workspace State

Understand how LLMs actually work, establish a local AI workshop, and configure your first tool gateway.

Demystifying LLMs — how the interpreter model thinks, context windows, and why reasoning degrades past the 25% threshold
OpenCode setup and Redstone Workbench Manager initialization
ICM orchestration — why file-system-based agent architecture outperforms black-box frameworks in production
Docker MCP Gateway configuration — native tool-use with zero custom code
Invariant knowledge bases — building a structured local wiki that outperforms vector RAG

Specialist Elective: Browser automation — AI-assisted data extraction from portals with SSO, 2FA, or no API

Day 2

Token Economics & Hybrid Automation

Master the economics of AI inference costs, build compression skills, and create deterministic automation pipelines.

Prompt caching & the "broken cache" problem — why dynamic schemas bust the cache and how to protect it
Token measurement — quantifying the actual cost footprint of your AI calls
Response compression — reducing context bloat with structured notation
n8n foundations — why systems should be 80–90% traditional code and only 10–20% AI
Building a hybrid pipeline: deterministic automation augmented with an AI inference node

Specialist Elective: Unstructured data extraction — parsing PDFs into structured JSON with Docling

Day 3

Advanced Tooling, Physical Actuation & Resilient Routing

Transition from manual coding to spec-driven compilation, implement multi-provider failover, and ship a complete system.

Spec-driven code compilation — writing declarative YAML specs that compile into production-grade, hallucination-resistant tools
Multi-provider failover — designing redundant AI routing chains that survive outages and rate limits
Physical hardware lab — using AI as a serial diagnostic co-pilot to reverse-engineer and control an embedded device
Capstone: End-to-end pipeline — n8n trigger → document processing → physical alert actuation → compressed wiki logging

Specialist Elective: Enterprise prompt version control — bundling and sharing tools via RBM package sync

How We Teach

Built for Retention, Not Passive Learning

Most AI training is a slide deck. Ours is structured around the conviction that you only truly learn a tool by using it under pressure, with a real goal.

L→L

Lecture → Lab Cadence

Every concept is immediately followed by a lab that applies it to a real task. No passive listening for hours before you get your hands on the tools.

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Zero Mandatory Homework

Everything is completed in class. Optional self-study materials are provided between sessions, but no student falls behind because of what they did after hours.

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Production Tooling

Students use the same tools, workflows, and stack Redstone Logic uses in production. Not simplified toy examples — the real thing, configured correctly.

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Specialist Electives

Each day has an optional specialist track for students who move quickly. Advanced topics including browser automation, data pipelines, and package management.

What Students Walk Away With

A Working System and the Skills to Build the Next One

By the end of the course every student has built and shipped a complete, working AI pipeline in class — not a demo, a real system.

Configured local AI environmentOpenCode, Docker MCP Gateway, Redstone Workbench Manager, and a working local workshop — all set up and operational.

Invariant knowledge baseA structured, cross-linked local wiki compiled from real source documents — ready to use as agent context.

Token cost awarenessAbility to measure, analyze, and reduce AI inference costs through cache protection and in-context compression techniques.

Hybrid automation pipelineA live n8n workflow that combines deterministic automation with an AI inference node for intelligent routing and classification.

Spec-compiled production toolA custom CLI tool generated from a declarative YAML spec and registered into their Docker Gateway — deployable immediately.

Capstone projectA complete end-to-end pipeline — document ingestion, AI analysis, physical actuation, and cache-aligned logging — built and delivered in class.

Course Formats

Available Now and Coming Soon

Courses are delivered on-site at your location. Travel and expenses are additional. Contact us to scope a custom engagement.

Available Now

AI Practitioner

3 days • ~8 hours per day • delivered weekly

Pragmatic AI Systems Engineering for technical generalists. From LLM fundamentals through a working hardware-integrated capstone. One day per week over three weeks.

$12,000

$4,000 / day • on-site • travel additional

In Development

AI Security Bootcamp

5 days • intensive format

A deeper engagement covering AI security fundamentals, threat modeling for AI systems, compliance frameworks, and certification preparation. Instructor certifications in progress.

Contact for details

Availability and pricing TBD

The Redstone Training Network

Train at a Network Facility or On Your Turf

The Redstone Training Network is a growing partnership with professional training facilities that host Redstone Logic courses. Network facilities are fully equipped and ready to run — no logistics on your end. We also travel on-site to organizations anywhere in the country.

Founding Facility — Huntsville, AL

Covenant Training Center →

Professional training facility and Redstone Logic founding partner. Available for AI Practitioner courses and future Redstone Training Network programs in the Huntsville area.

On-Site — Nationwide

Your Location

We bring the course to you. On-site delivery available anywhere in the country — travel and expenses are additional and scoped per engagement.

AI Training That Builds
Engineers, Not Prompt Users.