Building GenAI Applications in Healthcare
6 modules · 0 completed · ≈ 5 hours
Modules
Hands-on labs, not lectures
Building GenAI Applications in Healthcare
A build track. You assemble real request bodies, design JSON schemas, run simulated model calls, wire in retrieval from a governed knowledge base, declare and validate tools, compose a bounded agent loop, evaluate it against a fixed case set, and finish by taking one workflow end to end.
Your First Healthcare GenAI Application
Assemble an LLM API request that turns a synthetic consultation transcript into validated structured clinical output — then break it, diagnose it and fix it.
Grounding with Retrieval (RAG)
Attach a governed knowledge source to the request so answers cite something other than the model's memory — then evaluate retrieval and answer as two separate layers.
Tools & Function Calling
Declare tools, read a model's proposed call as an untrusted request, validate its arguments, gate every write behind human approval and handle failures without confusing intent with authorisation.
Build a Healthcare Agent
Compose instruction, tools, explicit state, limits and stop conditions into a bounded loop — then diagnose a runaway trace and fix it in policy rather than in prompt wording.
Evals for Healthcare GenAI
Build a small evaluation set with explicit expectations, split automated from human checks, run a baseline and a variant, read the failure slices and turn the result into a release decision.
Capstone: Ship a Bounded Healthcare GenAI Feature
Take one synthetic workflow end to end — scope, contract, grounding, tools, gates, validation, evaluation, release decision and handover — and defend every boundary.