HR policy helpdesk with retrieval and an evaluation set
Build a question-answering assistant over 40 HR policy documents, and prove with numbers that its answers are right and cited.
Industry project · for Northgate Analytics Centre, the Bengaluru capability centre of a UK retailer, 3,200 employees
Northgate's HR shared-services team answers about 1,800 employee questions a month. Roughly 40% are about leave, 25% about reimbursements and 15% about notice periods and transfers. Every answer is already written down in one of 40 policy PDFs, but employees can't find it, so they raise a ticket and wait two days.
Last year a vendor demo "chatbot" gave a confident, wrong answer about maternity leave in a town hall. The HR head now has one rule: no assistant goes live unless the team can show, on a fixed test set, how often it is right and that it cites the clause it used.
Your job is to build that assistant and the proof: - a retrieval pipeline over the policy documents (you will write a realistic synthetic set, because real policies are confidential); - an answer step that always cites document and section, and says "I don't know, please raise a ticket" when the policies don't cover the question; - an evaluation set of at least 60 questions with expected answers, and a script that scores the system on it.
Done looks like a small FastAPI service, a one-page evaluation report with retrieval hit rate, answer accuracy and refusal accuracy, and a short demo. HR will sign off if accuracy on the test set is at least 85% and the assistant never invents a policy.
GitHub repo with the service and tests + eval/questions.jsonl + one-page evaluation report (PDF or Markdown) + 3-minute demo video
Stack: Python, FastAPI, sentence-transformers, FAISS or Chroma, An LLM API, pytest
- Answers are correct and cited30
On your own test set at least 85% of answers are correct, and every answer names the document and section it came from.
- Refuses when it should15
Questions the policies do not cover get a polite refusal and a ticket suggestion, measured on at least 10 out-of-scope questions.
- Evaluation is honest and repeatable25
One command re-runs the evaluation, the numbers in the report match the script output, and failures are shown, not hidden.
- Engineering quality15
Clean FastAPI service, configuration in environment variables, tests for chunking and the refusal path, and a README that gets it running in under 10 minutes.
- Report for HR15
A one-page report the HR head can read: what it does, how accurate it is, where it fails, and what is needed before go-live.
Pass 60% · Merit 75% · Distinction 90%
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