Extractive QA on Clinical Trial Protocols

FreeVerified credential2 weeksAdvanced

Overview

What this challenge is about.

Extractive QA on Clinical Trial Protocols. Advanced challenge in code. Writing production code that solves real engineering problems, earn a blockchain-verif...

CredentialBlockchain-anchored

ShareableLinkedIn-ready

LanguageEnglish

PaceSelf-paced

The Brief

What you'll do, and what you'll demonstrate.

Build an extractive QA system over clinical trial protocols with strict verbatim-span guarantees and competitive F1.

This is not a coding exercise. It is the work a software engineer does between a Jira ticket and a merged PR. That distinction matters to every hiring manager who has seen candidates solve LeetCode problems and none who have shipped production code under real constraints.

When you finish, you will have something most graduates do not: a real-world deliverable, verified by Ewance, that you can show to a hiring manager and say "I did this. Here is the proof."

Earning criteria — what you'll demonstrate

Implement extractive QA via prompting and via fine-tuning, and compare them
Apply EM and F1 metrics correctly for span-extraction tasks
Build a verbatim-span guarantee at inference time
Communicate model trade-offs to non-ML domain customers

Program Fit

Where this fits in your program.

Sharpens the same skills your degree expects you to demonstrate.

Question Answering and Conversational Systems

Master · Nlp

Strong alignment

This challenge maps to Question Answering and Conversational Systems at the Master level. It sharpens the same practical skills your coursework expects — but in a real industry context with actual constraints and deliverables.

Skills

Skills you'll demonstrate.

Each one shows up on your verified credential.

Careers

Career paths this challenge builds toward

Completing this challenge demonstrates skills that transfer directly to these roles:

Career paths this builds toward

Canonical roles

NLP Engineer
Language is messy. People misspell, contradict themselves, ask the same thing five different ways, and expect a machine to understand. NLP engineers build the systems that try. The role spans classical text processing in spaCy, modern retrieval-augmented architectures stitched together with LangChain, and the constant judgment calls about when to fine-tune, when to prompt, and when to fall back to rules. It rewards people who love both linguistics and systems thinking. Students grow into it through small projects — a question-answering bot over their notes, a classifier for their inbox — that surface the real failure modes of language models. Good NLP engineers obsess over evaluation as much as architecture.
AI Engineering

NLP Engineer

Owning a span-extraction system with no-hallucination guarantees is the kind of disciplined NLP work pharma-adjacent companies hire for.

This challenge sharpens

extractive-qa
reading-comprehension
hallucination-prevention

Machine Learning Engineer

Fine-tuning an encoder model and shipping with a verbatim-span guarantee is core MLE craft.

This challenge sharpens

model-finetuning
pytorch
evaluation

Applied AI Scientist

Comparing prompt-engineering vs. fine-tuning and recommending one with documented trade-offs is applied-AI-scientist judgement work.

This challenge sharpens

extractive-qa
evaluation
hallucination-prevention

One more thing

You can put a credential on your CV by Friday.

Start this challenge