AI Training for Tech Teams

AI Training for Tech Teams is a structured, 4-week live cohort program by Metana that teaches software developers and engineers how to understand, apply, and build with generative AI tools in their day-to-day work — not just how to prompt a chatbot, but how to standardize AI usage across a whole team. The program is built for engineering teams of 5-50 people who already have access to AI tools like GitHub Copilot, Cursor, or Claude but lack a shared system for using them reliably. It covers four modules: standardizing AI usage and governance, LLM workflow and system architecture, evaluation and testing reliability, and agents, MCP, and production deployment. Each engineer walks away with 7 hands-on deliverables — including a shared prompt standard, a .cursorrules configuration applied to your real codebase, a reusable team eval harness, and a production-grade final project — built on your team's actual stack rather than generic exercises, so what they learn is immediately usable at work.

Using Copilot is a starting point, not a strategy. Research backs this up: 95% of AI pilots show no measurable ROI once evaluated beyond the initial pilot period (MIT, 2025), only 7% of AI spend goes toward the workforce while 93% goes toward technology itself (Deloitte, 2025), and 70% of employees are already using unvetted 'Shadow AI' tools with no governance or training (Microsoft, 2025). In other words, most teams already have the tools — what's missing is the system around them. According to the same MIT research, developers who understand the underlying principles of AI tools are significantly more productive than those who rely on them passively, which is exactly the gap this program closes. Instead of every engineer prompting differently with no shared standard, this program builds a shared prompting system, a governance policy covering shadow AI and tool approval, and an eval harness so AI-generated code gets tested before it ships.

The program has three enrollment tiers depending on your team's size and needs. Individual seats in the open monthly cohort are $2,000 per seat, letting you enroll 1-4 engineers into a scheduled cohort alongside engineers from other companies. If you want to enroll your entire team together, a private team cohort runs on your own schedule and is built around your actual codebase, priced custom based on team size — contact us for a quote. For larger organizations, an enterprise cohort for teams of 25 or more adds org-specific case studies and a dedicated instructor, also custom-priced. Across all three tiers, pricing is designed to be transparent upfront, with no hidden fees or surprise add-ons once you've enrolled. New cohorts open regularly, with upcoming open-enrollment start dates including April, May, and June 2026, or you can arrange a custom start date for a private or enterprise cohort. An advisor can help you figure out which tier fits your team size and budget.

Your team completes 7 hands-on deliverables throughout the 4-week program, all built on your real codebase rather than toy exercises. Specifically, each engineer walks away with: a shared, version-controlled prompt standard documented for the team; a .cursorrules configuration applied at the repo level across your actual codebase; an LLM workflow architecture template to use as a standard starting point for future AI-powered features; a reusable team eval harness with golden datasets and scoring criteria for testing AI output before it ships; a team AI governance document covering shadow AI policy, tool approval workflows, and prompt versioning; a production-grade final project built on your real stack; and a pre-rollout reliability checklist your team can reuse whenever you deploy a new AI feature. Because everything is built against your own codebase and workflows during the program, these aren't reference materials to adapt later — they're already integrated into how your team works by the time the cohort ends.

The program is designed to meet engineers where they are, which matters because most teams have a real split: junior developers who paste errors into ChatGPT and accept AI suggestions without much validation, and senior engineers who are skeptical of AI specifically because they've seen code get copy-pasted without real codebase context, testing get skipped, and review debt build up quietly until it breaks something. Rather than teaching a single one-size-fits-all workflow, the curriculum builds a shared system — governance standards, an eval harness, and a reliability checklist — that gives juniors clear guardrails while giving skeptical senior engineers a system they can trust, because they help validate it during the program rather than having it imposed on them. This is exactly why the program is built for teams of 5-50 with mixed AI-enthusiasm levels rather than assuming everyone starts from the same place: the goal is to get a team with very different comfort levels operating from the same governed system by the end.

AI Training for Tech Teams is built specifically for organizations, not individuals: it's designed around team cohorts of 5-50 engineers, works on your team's real codebase, and produces org-level artifacts like a shared governance document and a team eval harness rather than individual certificates. Pricing reflects that too, with per-seat, team, and enterprise tiers instead of a single individual price. By contrast, our other AI Training tracks — AI Training for Developers and AI Training for Professionals — are geared toward individual contributors upskilling on their own time, without the team-wide governance, manager visibility, or organization-wide rollout this program is built around. If you're an individual engineer looking to build personal AI skills, one of those individual tracks is likely the better fit; if you're a CTO or engineering lead trying to get your whole team using AI consistently and reliably — especially after a failed AI pilot that didn't scale — AI Training for Tech Teams is built for exactly that problem.

It's a 4-week online cohort program with a part-time time commitment of roughly 3 hours per week, designed to fit alongside a full engineering workload rather than compete with it. Each week includes weekly office hours — group live sessions with structured Q&A where your team works through real problems together — plus weekly assignment reviews, where your team gets direct expert feedback on the real work they've shipped that week, not generic critiques. Between sessions, live support is available on demand so nobody stays blocked waiting for the next scheduled call. Every session is recorded, and all templates, checklists, and systems your team builds during the program are yours to keep afterward, so nothing is lost if someone misses a live session. The cohort structure means your whole team moves through the material together on the same schedule, which is part of what makes the shared standards and governance documents stick — everyone is building the same system at the same time.

Yes. Beyond the open monthly cohorts — where individual seats cost $2,000 per seat and you can enroll 1-4 engineers — you can enroll your entire team in a dedicated private cohort scheduled around your own calendar rather than a fixed monthly start date. Private cohorts are built around your actual codebase from the start, so every module, from the .cursorrules configuration to the team eval harness, is developed against your real stack instead of a generic example repo. For larger organizations, enterprise cohorts for teams of 25 or more go a step further, adding org-specific case studies relevant to your industry and a dedicated instructor for the full 4 weeks. Both private and enterprise cohorts are custom-priced based on team size and scope, so contact us for a quote rather than the flat per-seat rate — worth considering if you want the whole team moving through the same material at once, built on your own code from day one.