Learn to Build, DeployAI Systems with LLMs

AI Training for Engineers is Metana's 4-week live bootcamp for working software engineers who want to build reliable, production-grade AI systems instead of relying on autocomplete they don't fully understand — a problem that affects the 60-70% of developers who currently ship AI-generated code without fully reviewing it. The program runs across four modules: Foundations & AI Workflow Integration, where you move from reactive prompting to structured prompts with role, context, and constraints; LLM Workflows & System Design, covering multi-step workflows with validation checkpoints; Evaluation, Testing & Reliability, where you build a reusable eval harness, since fewer than 20% of teams currently have one; and Agents, MCP & Production Systems, where you build agents with scoped tools, guardrails, and rollback triggers. You finish having shipped a production-ready internal AI tool and documented a version-controlled Developer AI Standard. Every session is built around real code, not theory or toy demos.

This program is built for working software engineers — frontend, backend, fullstack, data, and DevOps engineers who already write code professionally. It's a strong fit if you're already using Cursor, GitHub Copilot, or Claude Code day to day but want to get more value out of them, if you've shipped LLM-powered features without any testing or eval framework behind them, or if you want to build agents and MCP-connected tools safely instead of experimenting directly in production. Most participants are mid-to-senior engineers who want to stay ahead of the AI curve rather than fall behind it. It is not designed for product managers, designers, non-coding founders, or complete beginners — the curriculum assumes you can already read and write production code, since every exercise is built around real repositories rather than toy examples. Cohorts are capped at 8 participants so the instructor can give direct, specific feedback on your actual code and workflow, not generic advice.

The program costs $2,000 as part of the current pilot launch pricing, down from the standard $4,000 rate. There are no upsells and no tiered access — every participant, regardless of when they enroll, gets the exact same package: all four weeks of live instruction across the Foundations, LLM Workflows, Evaluation, and Agents/MCP modules, weekly office hours with structured Q&A, 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 recorded session. Cohorts are intentionally capped at 8 participants, so the price also reflects direct, individualized feedback rather than a webinar-style seat. Because this is a professional-development investment rather than a full bootcamp, many participants expense it through their employer's learning and development budget.

The program is part-time and built for engineers who are already working full-time, at roughly 3 hours per week spread across live sessions, weekly office hours, and assignments. That's a deliberately light footprint compared to a full bootcamp — the curriculum is compressed into 4 weeks specifically so you can fit it around an existing job rather than pausing your career to attend. Every live session, office-hours block, and assignment review is recorded, so missing a live block because of a work conflict doesn't mean missing the content; you can catch up on your own schedule. All templates, prompt libraries, and recordings remain yours for life once the cohort ends, so the 3-hours-per-week figure covers the structured portion, not a hard ceiling — many participants revisit modules and materials well after the 4 weeks are over as they apply what they learned to new projects at work.

The curriculum is built around the tools engineers already use day to day — Cursor, GitHub Copilot, Claude Code, ChatGPT, and Google Gemini — plus MCP (Model Context Protocol) for connecting agents to real tools and data sources. Rather than a tool-by-tool tour, the program teaches transferable skills that apply across whichever of these you standardize on: structured prompt engineering with explicit role, context, and constraints instead of reactive one-off prompting; configuring repo-level AI context and rules so tools understand your actual codebase; building and running eval harnesses so you can measure whether AI output is actually reliable before it ships; and designing agents with scoped tool access, guardrails, and rollback triggers so they can't go rogue in production. You also work directly in Git and GitHub throughout, since every exercise is built around real version-controlled repositories rather than isolated snippets disconnected from how engineering teams actually ship software.

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 production workflows without creating review debt, silent failures, or incidents. The shift is structural, not cosmetic: instead of reactive prompting, you learn structured prompts with role, context, and constraints; instead of AI writing code you don't fully own, you design the system AI executes within; instead of shipping LLM features with no evals, you build pipelines with real test sets and scoring rules; and instead of agents that can go rogue, you build agents with pre- and post-hooks, scoped tools, and rollback triggers. Every concept was built around one question: does this make a real engineer meaningfully better at their job? You leave with a documented, version-controlled, eval-backed AI system embedded in your actual workflow, not a certificate and a list of prompts you'll forget in a month.

Yes, team enrollments are welcome, and they're actually one of the more common ways engineers go through the program — a manager or tech lead books a block of seats so the whole team learns the same structured workflow, prompt standards, and eval practices at once, rather than everyone picking up scattered habits independently. Cohorts are intentionally capped at 8 participants total, which keeps the instructor able to give direct feedback on every person's actual code and assignments instead of lecturing to a large room, so team enrollments are subject to available seats in a given cohort rather than guaranteed on demand. If you want your team to go through together, either in a shared cohort with other participants or, for larger groups, across a couple of consecutive cohort starts, reach out at info@metana.io and we'll work out scheduling, invoicing, and seat allocation directly with you.

Cohorts run monthly and are capped at 8 participants each, which is a deliberate constraint, not a marketing number — it's what makes weekly assignment reviews genuinely individualized instead of rubric-based, and it's why office hours function as real structured Q&A rather than a large webinar. Upcoming cohort start dates include April 7, May 5, and June 2, 2026, with each cohort running the full 4-week curriculum across Foundations & AI Workflow Integration, LLM Workflows & System Design, Evaluation, Testing & Reliability, and Agents, MCP & Production Systems. Because seats are limited to 8 per cohort and demand has been consistent since launch, earlier start dates tend to fill first. The best way to lock in a specific cohort is to book a call with a Metana advisor, who can confirm current availability across the upcoming dates and walk you through any pilot-pricing discount tied to booking a free 1:1 consultation.