AI/LLM Training
Get your team building with AI instead of just talking about it.
What this is
Most AI training is either too theoretical or too superficial. We teach engineers how to actually build with LLMs — not just how to write a prompt, but how to design systems around models that are probabilistic, expensive, and occasionally wrong.
Our training covers the full stack of practical AI engineering: choosing the right model for the job, structuring prompts that work reliably, building agent workflows that recover from failures, and setting up evaluation frameworks that catch regressions before users do.
Everything is hands-on. Your team builds real components during the training, not toy examples. They leave with working code and patterns they can apply immediately.
Who it's for
Engineering teams that are adopting AI tools and need to move faster than trial and error allows. Teams where a few people have been experimenting with LLMs but the knowledge hasn't spread across the organization.
Technical leads and architects who need to make informed decisions about where AI fits in their stack. Developers who are comfortable with code but new to working with language models and their particular failure modes.
We work with teams from 4 to 40 engineers. The content adapts to your stack, your domain, and the specific problems your team is trying to solve.
How it works
Before we run a session, we learn your team. We want to know your stack, where your current AI experiments have stalled, and what you're actually trying to build — not a generic skills inventory. The curriculum gets built around those gaps.
Sessions run in half-day or full-day blocks over 1 to 3 weeks. Each one combines instruction with hands-on exercises using your actual codebase and tools where possible. We don't use toy examples when the real problem is available.
Between sessions, your team builds with what they've learned. We stay available for async questions and review their first AI implementations as they go. The goal is making sure the training lands in production, not just in notes.
What you get
Your team gets practical skills they can use immediately. Each training module comes with reference materials, code templates, and pattern libraries specific to the topics covered.
We deliver a customized playbook for your organization covering recommended tools, model selection guidelines, prompt patterns for your domain, and evaluation strategies. This becomes your team's reference for AI development going forward.
After the formal training, we include a 2-week follow-up period for async questions and code review on your team's first AI implementations. The goal is making sure the knowledge sticks and gets applied to real work.