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AI training in the canton of Zurich

In the canton of Zurich, AI training meets the needs of finance, technology and healthcare teams: comparing several answers, tracing sources and documenting why a proposal is accepted or rejected before any decision.

Start with real work and a local context

What follows is a fictional scenario built for the workshop. It describes neither a client nor an achieved result: it helps choose, with you, an exercise close to your daily work and define in advance what counts as a good answer.

In Zurich, a case can involve a decision memo for finance and technology teams: compare AI responses, trace sources and document why a proposal is accepted or rejected. Reasoning quality matters as much as drafting speed.

Which AI training path fits your needs?

AI on the Job — Zurich
Identify a real task, write an instruction and compare the output with reference work.
Managing in the AI Era — Zurich
Decide what can be assisted, who checks quality and when an error must be escalated.
Leading AI Adoption in Teams — Zurich
Select a pilot use case, define evaluation criteria and organise team feedback.
Building No-Code AI Assistants and Agents — Zurich
Scope an assistant, restrict its sources and permissions and test its behaviour before deployment.

Claude AI training in the canton of Zurich

For Claude AI training in the canton of Zurich, Claude, Anthropic’s assistant, can support a workshop when your organisation permits its use. The aim is not brand promotion: participants learn to scope a task, supply sufficient context and examine the result. Tool selection and features are confirmed during scoping according to available access.

The typical Claude exercise uses an invented dossier: the instruction forbids asserting anything beyond the supplied documents. Participants spot unfounded additions, rephrase their request with audience and constraints, then check that the second answer stays faithful to the facts.

Write and refine an instruction for a workplace task.
Analyse an authorised document and verify the passages used in the answer.
Compare responses, detect omissions and correct unsupported claims.
Define human approval before reusing an output at work.

Claude is an Anthropic trademark. This page claims neither an Anthropic partnership nor an official Claude certification. No Claude subscription is advertised as included.

A learning process built around verification

Select a representative work case and state its quality criteria.
Experiment on a fictional or anonymised dossier, keeping a record of attempts.
Check every assertion against the sources and log the validation.
Set which uses are allowed at work, which are excluded and which need reassessment.

Data, errors and human supervision

Three rules are set before any exercise: the perimeter of permitted data, the reference documents, the person responsible for validation. No real data is used in workshops. Fluency guarantees neither accuracy nor compliance.

Prepare your AI training

Specify the roles involved, participants’ experience, permitted tools and a representative task. Do not send confidential documents with your enquiry. Delivery arrangements, working language and venue are agreed with SHR; a service city is not an SHR office.