AI in customer service
MET-C06M01
Ground answers (RAG), mask data, escalate to a human.
Deploy a reliable virtual agent: grounded answers, masked data and handoff to a human.
Training by function
A virtual agent speaks on behalf of the company. This program teaches how to ground its answers, mask sensitive data and plan escalation to a human.
Why this training
An ungrounded assistant invents policies, promises refunds or discloses another client's data. The defense: ground answers in a verified knowledge base (RAG), mask sensitive information and guarantee a handoff to a human. This program installs those guardrails.
Coming soon in the « AI by profession » specialization: this module is not available yet. The common core and the modules to go deeper below are available today.
MET-C06M01
Ground answers (RAG), mask data, escalate to a human.
Deploy a reliable virtual agent: grounded answers, masked data and handoff to a human.
The baseline reflexes your whole team shares, whatever the job.
C01M01
Demystify the probabilistic engine and distinguish it from predictive AI.
Understand the fundamental principles and key mechanisms of how generative AI works.
RegisterC01M02
Identify fabricated information and how to verify it at the source.
Identify the causes of AI hallucinations and apply a rigorous method to guard against them.
RegisterC01M03
Structure your prompts (Role + Context + Task + Format) and iterate.
Write structured prompts (RCTF) and iterate effectively to get precise results.
RegisterC01M04
Sort your data and de-identify rigorously before every prompt.
Categorize work data and apply anonymization techniques to protect sensitive information.
RegisterC01M05
Recognize shadow AI and have tools approved by IT.
Identify the risks of shadow AI and favour officially approved tools.
RegisterC01M06
Apply "human-in-the-loop" and the "four eyes" method.
Take professional accountability for generated content and apply the principle of human verification.
RegisterThe modules most relevant to the risks specific to your function.
C01M04
Sort your data and de-identify rigorously before every prompt.
Categorize work data and apply anonymization techniques to protect sensitive information.
RegisterC04M04
Know when disclosure is required and how to phrase it.
Set the rules for disclosing AI use and include the appropriate transparency notices.
RegisterC02M02
Distinguish reversible masking from legal, irreversible anonymization.
Distinguish pseudonymization from anonymization and prevent re-identification through cross-referencing.
RegisterEnroll your teams in our AI-literacy and governance training, from the common core to function-specific use cases.
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