Engineers Clinic
IntermediateSoftware trackFP-AI-01

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.

AI, Data & AnalyticsRetrieval-Augmented Generation (RAG)
7 Guided Steps62h Internship TierSubmit: GitHub repo with the service and tests + eval/questions.jsonl + one-page evaluation report (PDF or Markdown) + 3-minute demo videoVerified CertificateAI Rubric Graded
Program Overview

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.

Retrieval-augmented generationLLM evaluationPrompt designVector searchAPI developmentTechnical writing
What you submit

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

How it is graded
  • 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%

Learning Outcomes
Build a retrieval pipeline and explain chunking choices
write an evaluation set and score an LLM system against it
make an assistant refuse when it does not know
report accuracy honestly to a non-technical owner
Target Audience

Engineering Students

Diploma, B.Tech, B.E., BCA, MCA, or M.Tech students seeking practical experience.

Aspiring Developers

Developers who want to build proof-of-work capstone projects for tech resumes.

Job Seekers

Graduates looking for AI-evaluated code review & verified industry credentials.