AI LAB
HR Handbook Assistant with Grounded Retrieval
Build a project on an HR handbook assistant that answers policy questions grounded in real source text
Verified Certificate on Successful Completion
What is in it for you?
LLM tokens included
A metered token budget is bundled in, call real models from your code with no API key or extra cost.
AI mentor on tap
Stuck? An in-IDE AI mentor gives hints and debugging help, without handing you the answer.
Instant grading
Submit and get an objective, rubric-based verdict in seconds, pass, or actionable feedback.
Verified certificate
Pass and earn a shareable, verifiable certificate you can add to your LinkedIn profile.
Zero-setup cloud IDE
A ready sandbox with the libraries pre-installed, start building in the browser instantly.
Retry until you pass
Iterate as many times as your budget allows; a fail keeps the lab open to try again.
Grab your slot before the offer expires
Start solving today!
Basic Info
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Project Statement
Business Problem
Meridian Corp's people team fields hundreds of repetitive handbook questions each week — leave balances, reimbursement caps, parental leave — pulling roughly 15 hours of HR time weekly away from strategic work. A generic chatbot is worse than nothing: when it invents a policy, it creates compliance risk and erodes employee trust. The team needs answers that are fast, correct, and provably grounded in the actual handbook.
AI Solution
You'll build a retrieval-augmented assistant that chunks the handbook, embeds each chunk into a vector index, and retrieves the most relevant passages for every question. The model then answers using only those retrieved passages — and explicitly directs employees to HR when the handbook is silent, instead of guessing. Your chunking strategy directly drives retrieval quality and answer accuracy.
Potential Impact
A grounded assistant can deflect the bulk of routine handbook queries, reclaiming an estimated 10+ HR hours per week and delivering instant answers to employees. Because responses stay tied to source text, hallucinated policy claims drop toward zero, cutting compliance exposure. Faster, trustworthy answers lift employee self-service satisfaction while freeing the people team for higher-value work.
Skills that you will build
Solve a real AI-engineering problem that builds in-demand skills and a portfolio-ready, verified certificate.
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Document chunking strategy (size, overlap, granularity)Generating and using text embeddingsBuilding an in-memory vector index with FAISSSemantic retrieval of relevant contextRetrieval-augmented generation (RAG) groundingPreventing hallucination with source-only answersDesigning safe fallback behavior for out-of-scope queriesAI Lab Pre-requisites
- Comfortable writing Python functions
- Basic understanding of what embeddings are
- Familiarity with calling an LLM API
Target Roles
- AI Engineer
- LLM Application Developer
- RAG Engineer
- AI Solutions Architect
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Frequently asked questions
Are LLM tokens included?
Yes. Every lab runs with a metered LLM budget included, so you can build and test without any extra setup.
What will I earn?
A verified, shareable credential you can add to your resume or LinkedIn once your solution passes the auto-grader.
Do I need prior RAG experience?
No. This L1 lab is designed as an approachable first RAG project — comfort with Python and basic LLM calls is enough.
Will the answer be given to me?
No. You design the chunking, retrieval, and grounding logic yourself. An AI mentor is available to guide you if you get stuck.
Why does chunking matter here?
Your chunk size, overlap, and granularity directly affect what gets retrieved — and retrieval quality determines whether answers are accurate and grounded.
How long does this lab take?
Most learners complete it in about 60 minutes, depending on experience.