AI LAB
Clinical Guideline Copilot with RAG
Build a project on a clinical guideline copilot that answers with RAG and refuses to guess when guidelines don't apply
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
CareLoop's 400+ clinicians waste an estimated 15 minutes per shift hunting through stale PDF guideline libraries, and general-purpose chatbots hand back plausible-sounding but ungrounded, uncitable medical answers. In a clinical setting, a single fabricated dosage can trigger a patient-safety incident and regulatory exposure. The organization needs answers that are traceable to the current guideline source or an explicit refusal.
AI Solution
You build a RAG pipeline that chunks the live guideline library, indexes it in a vector database, and retrieves the most relevant passages for each clinical question. Answers are generated strictly from retrieved text and carry a citation back to the source guideline line. When retrieval doesn't clearly cover the question, the copilot escalates to a clinician rather than guessing.
Potential Impact
A grounded, citable copilot can cut guideline-lookup time from ~15 minutes to under a minute per query, freeing thousands of clinician-hours per month across a large practice. Cited answers make every response auditable, and the built-in escalation path eliminates the biggest risk of clinical LLMs — confidently wrong advice — protecting patient safety and CSAT.
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 embeddings into a vector databaseBuilding a Retrieval-Augmented Generation (RAG) pipelineGrounding LLM answers strictly in retrieved contextAdding source citations to generated answersImplementing safe refusal and escalation behaviorPreventing hallucination in high-stakes domainsPrompt engineering for grounded question answeringAI Lab Pre-requisites
- Comfortable writing Python functions
- Basic understanding of how LLMs and prompts work
- Familiarity with the idea of embeddings or vector search (helpful, not required)
Target Roles
- AI Engineer
- LLM Application Developer
- RAG Engineer
- AI Solutions Architect
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Frequently asked questions
Are LLM tokens included?
Yes. The lab runs in a cloud IDE with a metered LLM budget included — no separate API key or billing needed.
Do I need medical knowledge to complete this?
No. The clinical guidelines are provided in the lab. Your job is the AI engineering: chunking, retrieval, grounding, citation, and safe escalation.
What do I earn?
A verified, shareable credential you can add to your resume or LinkedIn once your solution passes the automated grader.
Will this teach me to prevent hallucinations?
Yes. A core goal is building a copilot that answers only from retrieved sources and explicitly refuses or escalates when the guidelines don't apply.
How is my work evaluated?
An auto-grader checks that your pipeline ingests and retrieves correctly, grounds answers with citations, and escalates on out-of-scope questions instead of fabricating.
How long does it take?
Most learners finish in about 90 minutes, depending on your familiarity with RAG concepts.