Context and objectives
The HR team received an average of 120 questions per week from employees: leave, health insurance, training, pay, onboarding. 80% of these questions had answers in existing documentation, but the documents were scattered and poorly accessible.
The technical architecture
We chose a RAG architecture: HR documents indexed in a vector database. At each question, relevant passages retrieved and injected into the LLM context. Stack: LangChain, Chroma, GPT-4o, Microsoft Teams, deployed on Azure.
Critical point: we added a systematic note at the bottom of each response: 'This response is based on current HR documentation. In case of doubt, contact your HR manager directly.' Essential for compliance and trust.
Surprises (good and bad)
Good surprise: immediate adoption
Contrary to fears, employees adopted the tool very quickly, especially shift workers who had no real-time HR access.
Bad surprise: out-of-scope questions
The assistant received very personal questions we had not anticipated: harassment situations, health problems, manager conflicts. We quickly added detection logic and redirection to a human for these sensitive cases.
Building an internal assistant like this one — RAG over business documents, guardrails, production rollout — is the through-line of the AI Engineering Bootcamp.
