Claude Code training: what your team needs, and what's already free

Start with the free courses, because they're good

If you're searching for Claude Code training, the first thing worth knowing is that Anthropic gives a lot of it away. Claude Academy publishes structured courses including Claude Code 101. Anthropic also runs Claude Code in Action, which covers scoping and steering longer sessions rather than one-off tasks, and freeCodeCamp has a full course of its own.

These are good. Better than most paid introductions, and written by the people who built the tool. If your team hasn't been through them, buying training from anybody is premature. Send the links round, give people a fortnight, and see who actually does it. That alone tells you something useful about who to train first.

What a course can't cover

Courses teach on clean example projects. Yours isn't one. It has the inherited script nobody documented, the deploy step that only works on one person's laptop, and the config file that fails quietly when it fails at all.

That gap shows up the first time somebody points Claude Code at real work. Our first week with Claude Code walks through the sequence that avoids the worst of it: read before you write, then one small task, then one connected tool. The VS Code setup guide covers the install itself. Neither one replaces somebody sitting with your team while they do it on your codebase.

The scarce skill is review, not prompting

Prompting is easy to teach and most people have it inside a day. What takes longer is knowing when the answer is wrong.

Claude Code sounds equally confident either way. A change that compiles, passes lint and does the wrong thing is the normal failure, not the exotic one. Catching that reliably takes practice on work you care about, ideally with somebody experienced telling you when you've been too trusting. Non-developers can genuinely build software with Claude, and the ones who do it well built that instinct on purpose rather than by accident.

What happens after it works

The awkward problems start once something works. A prototype somebody put together in an afternoon becomes the thing four people rely on, and nobody has decided where it runs, who patches it, or what happens when its author moves teams.

Building an MVP with Claude covers the line between a prototype and a product, which mostly comes down to error handling, data you can't afford to lose, and somebody owning it. Building business apps with Claude covers the governance that stops a genuinely useful internal tool turning into shadow IT six months later.

Rolling it out across a team

Teaching one person and rolling out to thirty are different jobs. Four things worth settling before anybody starts:

• Access: who gets it, on which repositories, and whether the agent can run commands or only propose them.
• Data boundaries: what the agent never sees. Decide this before somebody connects a tool, not after.
• Conventions: a file in the repo telling the agent how you do things, so five people don't end up with five house styles.
• Done: what review an agent-written change gets before it merges, and who signs it off.

So do you need training, or a fortnight of practice?

An honest test. If nobody on the team has shipped anything with Claude Code yet, you don't have a training problem. You have a nobody-has-tried-it problem, and the fix is a free course plus protected time in the diary.

If several people have shipped things and the results are all over the place, that is a training problem. Some of them are reviewing carefully and some are approving whatever appears on screen. That gap doesn't close on its own and it's the one worth paying to fix.

One more thing worth being straight about. If what your team really wants is a specific internal system, a quoting tool or an approvals workflow or whatever it happens to be, work out which thing you're buying: training so your own people can build it, or somebody to build it for you. Those cost different amounts and they leave you in very different places afterwards. Connecting real systems is where this gets useful and risky at the same time, and giving your AI a company brain with MCP covers doing that deliberately.