AI Training for Employees and AI Workshops for Companies

What AI training for employees is, and what it is not

AI training for employees is a focused learning process meant to change how people work, not just to show them what the tools can do. In good training, participants understand how a language model arrives at an answer, where it tends to go wrong, what information they are allowed to give it and how to check what comes back. Only on that foundation do they practice with the tools themselves.

What it is not: a demo of dozens of chat tricks, a pile of prompts to copy or a slide deck about the future of work. Those generate excitement for a day or two, and then everyone drifts back to old habits. The real gap in most organizations is rarely access to the tools. It is knowing when to use them, how to fit them into an existing process and how to avoid creating double work fixing wrong outputs.

Training like this usually has three layers. The first is understanding: what the tools do and how they work under the hood, deep enough to make good decisions. The second is skill: practicing real tasks until the work feels natural. The third is working rules: what is allowed, what needs review and who owns the output. Without the third layer, even a highly skilled team keeps working each in their own way.

Who an AI workshop for companies is for

A workshop like this suits any team that already runs into AI day to day and wants to move from ad hoc use to a way of working. In practice, most requests come from one of these situations:

If it is not yet clear what the team needs, that is perfectly fine. The fit call at the start exists for exactly that: to find where AI can save real work, where it might add risk and which format will get you to the result fastest.

• Dev and QA teams that were given Claude Code, GitHub Copilot or a similar tool and want to use it without hurting code quality.
• DevOps and infrastructure teams that want to bring AI into writing Terraform, troubleshooting incidents and working with CI/CD pipelines.
• Leadership and team leads who need a sober picture of capabilities and risks before deciding on tools and budgets.
• Non-technical teams such as operations, support or HR that want repeatable AI workflows instead of reinventing the wheel every time.
• Organizations planning to roll out an internal AI platform that want employees to arrive ready for it.

Talk, hands-on workshop or company AI course: choosing a format

There are three main formats, and each solves a different problem. The choice should follow from what you want to happen after the training, not just from how much time is free on the calendar.

A simple rule of thumb: if the goal is to start a conversation about AI in the organization, a talk is enough. If the goal is for a specific team to start working differently, you need at least one hands-on workshop, and ideally a program with a follow-up session.

You can also combine them: an opening talk for everyone, followed by separate workshops by role. That way leadership and teams share a vocabulary, and each group practices what is relevant to it.

• A talk on artificial intelligence: a shorter session for a broad audience, suited to kicking off a process, a team day or leadership. The goal is a shared language: what models do well, where they fail, what an AI agent is, what RAG is and what responsible work with company data looks like. A talk does not replace practice, but it sets expectations.
• A hands-on workshop: laptops open, with every participant working on a real or near-real task. This is where habits form: how to frame a task for the tool, how to break down a big problem, how to check an output and what to do when it is wrong.
• A company AI course or multi-session program: a series with assignments between sessions. Each session returns to what happened in actual work, fixes what broke and goes deeper. This format produces the deepest change, because habits are built over time rather than in a single day.

AI agents workshop for developers: Claude Code and GitHub Copilot training for your team

For developers, AI stopped being just autocomplete a while ago. Tools like Claude Code and GitHub Copilot can read code, run commands, edit several files at once and act as an agent across an entire task. That is a lot of power and also a source of problems: sweeping changes nobody really reviews, tests that adapt themselves to the code instead of checking it, and an agent that guesses because it has no context about the project.

An AI agents workshop for developers focuses on this kind of work inside the team's own repository, or one that mirrors it. Topics are chosen to fit the need and usually include:

The goal is not for each developer to write less code, but for the whole team to work with agents predictably: every change gets reviewed, tests keep protecting the system, and a new developer joining the project understands the rules without anyone explaining them verbally. It usually turns out that a small investment in documentation and repository structure improves the agent's work more than any clever prompt wording.

• Preparing the repository for agents: instruction files such as CLAUDE.md or Copilot instructions, a clear folder structure and documented build and test commands.
• Breaking a task into small, checkable steps instead of one large request that is hard to review.
• Reviewing AI-assisted code: what to look at, what signals a problem and how to keep a human accountable for every change.
• Testing, CI/CD and permissions: where the agent runs, what it can access and what stops it before it causes damage.
• An introduction to building AI capabilities yourselves, including RAG, tool calling and multi-agent workflows, for teams that want to build such products, not just use them.

How a training is built: from the first call to the day after

Every training goes through the same stages, even a single talk. What changes is how deep each stage goes.

1. Fit call

   We clarify who the audience is, their background, which tools the organization uses and what should change after the training. This is also where constraints come up: what data may be shown, which tools are approved and what environment participants will have.

2. Setting goals

   Together we spell out what participants will be able to do at the end. Not a vague goal like getting familiar with AI, but something like breaking a development task into steps and handing it to an agent, or building a reusable workflow for a specific process.

3. Program and exercises

   We pick examples from the team's world, prepare a practice environment and design exercises that include cases where the tool gets it wrong. Where needed, synthetic data replaces real information.

4. The session itself

   A short explanation, a demo, then individual or pair work with close support. Most of the time goes to practice and the questions it raises.

5. Wrap-up and next steps

   We sum up what worked, which working rules the team adopts and what comes next. In a multi-session program, this is also where the assignment for the next session is set.

What participants leave with and how progress is measured

At the end of good training, participants leave with three things: something they built themselves, working rules the team agreed on and materials they can come back to. Depending on the workshop, the output might be a workflow template, a script, a repository ready for agents or a documented process.

Progress is measured against the goals set at the start, not by gut feeling. During the workshop we check whether participants completed the exercise, whether they can explain why the output is correct and whether they notice when the tool is wrong. In a multi-session program we can also look at changes in the work itself: which tasks now go through AI, where it saved time and where it actually created extra work.

It is important to define success in advance. An organization that wants to get to know the field needs a different measure than one that wants every developer working with an agent by the next sprint.

How to choose an AI trainer, and mistakes worth avoiding

The AI training market is crowded, and it is not easy to tell someone who knows the tools up close from someone who has read about them. A few questions worth asking before you choose:

And common mistakes to avoid: choosing a format based on free time rather than the goal; putting developers and marketers in the same workshop and expecting both to leave satisfied; teaching a tool the organization has not yet approved; and ending the training without anyone owning adoption the day after.

• Does the trainer build AI systems, or only present them? People who ship solutions to production can tell you where they break.
• Is the program tailored to your team, or is it the same deck for every audience?
• How much of the time is actual hands-on practice?
• Does it address data security, permissions and output review, or only capabilities?
• What remains after the training: materials, exercises, a way to ask questions?
• Is the trainer willing to say when AI is not the right answer? Someone selling only excitement will not help you avoid mistakes.

Why train with me

I am an AI Solutions Architect: I design and build end-to-end AI solutions, including RAG pipelines, agentic and multi-agent workflows and LLM-based organizational platforms, and take them from concept to production. So the examples in the workshop come from real work, including the places where things did not work the first time.

Before that I worked as an automation developer, led automation development technically in C# and CI/CD, and moved into a DevOps Engineer role, working hands-on with Kubernetes, Terraform, Docker, Azure and Azure DevOps. That background matters when training developers: I know the pipeline, the permissions and the infrastructure an agent touches, not just the tool itself.

I also bring teaching experience: I was an assistant lecturer in a software engineering course at Ariel University. And I work independently, so the person who plans the training with you is the same person who delivers it.