AI LAB
Compliance Copilot: RAG over Regulations
Build a project on a compliance copilot that answers regulatory questions with cited, RAG-grounded rules
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, has staff fielding 400+ compliance questions a week against a body of regulations that changes almost monthly. Analysts spend 15-20 minutes hunting through dense rule documents per query, and a wrong answer on KYC timelines or retention rules risks regulatory penalties running into lakhs. Generic AI assistants only make it worse by inventing rules and citing nothing.
AI Solution
You'll build a Retrieval-Augmented Generation copilot that chunks the live regulations file, ingests it into a vector store, and retrieves the most relevant rules for any question. The model then answers only from what it retrieved and cites the specific rule ID behind every response, so staff can trust and verify each answer instantly.
Potential Impact
Grounded, cited answers cut per-question research from ~18 minutes to under 30 seconds, freeing an estimated 90+ analyst hours a month. Citation-backed responses slash hallucinated compliance advice toward zero, and because the pipeline re-ingests updated rules automatically, the bank avoids costly rebuilds every time regulations change.
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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Designing document chunking strategies for retrievalIngesting and indexing text in a vector databaseSemantic search and relevant-context retrievalBuilding end-to-end RAG pipelinesGrounding LLM outputs to eliminate hallucinationEnforcing source citations in generated answersStructuring maintainable, scalable knowledge basesAI Lab Pre-requisites
- Comfortable writing Python functions
- Basic understanding of how LLMs and prompts work
- Familiarity with the idea of embeddings or vector search is helpful but not required
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 on a metered LLM budget that's included, so you can build and test without setting up your own API keys.
What do I earn when I finish?
A verified, shareable credential you can add to your resume and LinkedIn to prove you can build grounded RAG pipelines.
Do I need prior RAG experience?
No. If you're comfortable with Python and understand basic LLM prompting, this lab will walk you through building a full retrieval-augmented pipeline.
Is this a course with lectures?
No. It's a single hands-on problem you solve in a cloud IDE, with an AI mentor available and auto-grading on your work.
Will I need to install anything?
No. Everything runs in a locked-down cloud IDE with the vector client and LLM access pre-configured.
How is my work evaluated?
An automated grader checks that your copilot retrieves the right rules and returns accurate, correctly cited answers.