AI LAB
Compliance Copilot: RAG over a Regulatory Corpus
Build a project on a compliance RAG copilot that answers regulatory questions with cited, source-grounded rule IDs
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
Cred North, a fast-growing neobank, maintains hundreds of frequently-updated compliance rules that staff must reference daily. Support and operations teams field over 400 compliance queries a week, and manually searching regulations takes 8-12 minutes each — with a real risk of citing outdated or wrong rules, exposing the bank to regulatory penalties.
AI Solution
You will build a retrieval-augmented copilot that chunks the regulatory corpus by rule, embeds each unit into an in-memory vector index, and retrieves the most relevant rules for any question. The model then generates an answer grounded only in the retrieved rules and cites the exact supporting rule ID, so answers stay correct even as regulations are added or revised.
Potential Impact
Query resolution drops from ~10 minutes to seconds, saving an estimated 55+ staff hours per week. Grounded, citation-backed answers cut misinterpretation errors dramatically and give auditors a verifiable trail — reducing compliance risk while scaling with the corpus at zero re-training cost.
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 for retrievalText embedding generationBuilding an in-memory vector index with FAISSSemantic similarity search and retrievalGrounding LLM answers in retrieved contextCitation and source attribution in generated answersPreventing hallucination and hardcoded outputsDesigning RAG pipelines over evolving corporaAI Lab Pre-requisites
- Comfortable writing Python functions
- Basic understanding of embeddings and vectors
- Familiarity with how LLM prompts work
Target Roles
- AI Engineer
- LLM Application Developer
- RAG Systems Engineer
- AI Solutions Architect
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Frequently asked questions
Are LLM tokens included?
Yes. Every lab runs with a metered LLM budget inside the cloud IDE, so you can build and test without any external API keys.
What do I earn on completion?
A verified, shareable credential you can add to your resume and LinkedIn to prove your RAG engineering skills.
Do I need prior experience with vector databases?
No. Basic Python and a general sense of embeddings are enough — you'll learn to build a vector index and retrieval flow hands-on.
Is this a multi-week course?
No. This is a single focused, hands-on lab you solve in one sitting in a locked-down cloud IDE with an AI mentor to guide you.
Will my solution break when the regulations change?
That's exactly the point — you build a retrieval-based system that adapts to a growing, changing corpus without hardcoding or re-training.