Learn to Build, DeployAI Systems with LLMs

AI Training for Developers is a 4-week, live, part-time bootcamp built by Metana for working engineers who want to build reliable, production-grade AI systems rather than just lean on autocomplete. Across four modules, you move from foundations and AI-workflow integration, into multi-step LLM workflow and system design, into evaluation and testing (the gap 60-70% of developers currently have — relying on AI-generated code they don't fully understand, with under 20% having any evals layer at all), and finally into agents, MCP, and production systems. Every session is built around real code, not slideware: you leave having designed structured prompts, built a reusable eval harness, shipped an agent with scoped tools and guardrails, and documented a version-controlled Developer AI Standard for your own codebase — a production-ready internal AI tool, not a toy demo built just to prove a concept in class.

This program is built for working developers — frontend, backend, fullstack, data, and DevOps engineers — who already write code professionally, typically mid-to-senior engineers who want to stay ahead of the AI curve rather than fall behind it. It's aimed squarely at people who are already using tools like Cursor, GitHub Copilot, or Claude Code day-to-day but want more value out of them than ad hoc prompting delivers, and at engineers who've shipped LLM-powered features with no real testing framework behind them and want to close that gap before it causes a production incident. It's also a fit if you want to build agents or MCP-connected tools safely, with pre/post hooks, scoped access, and rollback triggers rather than guesswork. It is not built for PMs, designers, non-coding founders, or complete beginners — the curriculum assumes you can already read and write production code, since every exercise is done inside a real codebase, not a sandboxed tutorial environment.

The program is $2,000 for the current pilot launch, down from the standard $4,000 price. There are no upsells, no tiered access, and nothing held back for a higher-priced package — every participant gets the full experience: all 4 weeks of live instruction, weekly office hours with structured Q&A and peer learning, weekly assignment reviews with real expert feedback rather than rubric-based grading, live on-demand support for mid-week questions, a final project build clinic with peer review, and lifetime access to every template, prompt library, and session recording. Because cohorts are capped at 8 participants, that price also buys real, individual feedback on your work rather than a seat in a large webinar-style course where you're mostly watching. If your employer offers a Learning & Development stipend or tuition reimbursement, an advisor can provide the documentation you'd need to submit for that as well.

The program is deliberately part-time and built around a full-time engineering job, at roughly 3 hours a week across live sessions, office hours, and assignments — closer to a focused weekly commitment than a second job. That 3 hours covers weekly office hours with structured Q&A, an assignment review with real expert feedback, and any live on-demand support you need mid-week; the final project build clinic in week 4 asks for a bit more focused time as you ship your internal tool. All live sessions are recorded, so if you miss a block because of a work deadline, you can catch up on your own schedule without falling behind the cohort, and every recording, template, and prompt library remains yours for life afterward. That low weekly footprint is intentional: the program is designed for engineers who are already stretched thin day-to-day and need training that fits around real production work, not one that competes with it for your time.

The curriculum is built around the tools working engineers actually use day-to-day: Cursor, GitHub Copilot, Claude Code, ChatGPT, Google Gemini, and MCP (Model Context Protocol), plus exposure to newer AI-native builders like Lovable, Replit, Windsurf, and v0, alongside your existing Git and GitHub workflow. But the program isn't a tool-by-tool tutorial — it's built around the underlying skills that transfer across all of them: moving from reactive prompting to structured prompts with explicit role, context, and constraints; configuring repo-level AI context and rules for your own codebase; building and running evals so you catch failures before they ship; and building agents with scoped tool access, pre/post hooks, and rollback triggers so an agent's actions stay within the boundaries you design rather than going rogue. The goal is that whichever of these tools your team standardizes on, or if that changes next year, the underlying system you build in the program keeps working.

Most AI courses teach you what large language models are and how to write a better prompt. This program, built by Metana specifically for working engineers, teaches you how to integrate AI into real engineering workflows without creating review debt, silent failures, or production incidents — the actual failure modes teams hit once AI-generated code leaves a demo and reaches production. Every module was built around one question: does this make a real engineer meaningfully better at their job, not just faster at typing. You don't leave with a certificate and a list of prompting tips; you leave with a documented, version-controlled, eval-backed AI system embedded in your actual workflow — a Developer AI Standard, a repo-level AI setup, a multi-step LLM workflow with validation checkpoints, a working eval harness, an agent built with guardrails on MCP, and a shipped internal tool you built during the program's final build clinic with real peer review, not a graded rubric.

Yes — team enrollments are welcome, and it's a common way engineering leads bring this training in-house rather than sending one engineer through and hoping the knowledge spreads informally afterward. Because cohorts are intentionally capped at 8 participants total, team enrollments are limited by the same cap that keeps the program from turning into a webinar-style seat — everyone, including your team members, gets real feedback on their assignments and final project rather than passive lecture time. That cap also means a team of several engineers going through the same cohort together ends up sharing the same Developer AI Standard and eval conventions by the time they finish, which is harder to get from individual, staggered enrollment. If you're considering enrolling multiple engineers at once, reach out at info@metana.io to arrange team enrollment, confirm seat availability for an upcoming cohort start date, and discuss whether a private cohort makes sense for a larger group.

Cohorts run monthly and are capped at 8 participants each, by design, so every engineer gets real feedback on their weekly assignments and final project rather than a passive seat in a larger course. Upcoming cohort start dates currently on the calendar include April 7, May 5, and June 2, 2026, with a limited number of seats available in each one given the 8-person cap; once a cohort's seats are filled, you'd be booked into the following month's start date instead. Because seats are limited and cohorts start monthly rather than on demand, waiting to enroll can mean waiting for the next available start date rather than beginning right away, especially for team enrollments that need several seats in the same cohort. Book a call with an advisor to check current seat availability for the next cohort, reserve your spot, and find out about the discount currently available for booking a free 1:1 consultation with a Metana expert.