Design Prompt Versioning and Observability for a Coding Assistant
Overview
What this challenge is about.
Design Prompt Versioning and Observability for a Coding Assistant. Advanced challenge in code. Writing production code that solves real engineering problems,...
The Brief
What you'll do, and what you'll demonstrate.
Make prompt changes observable and versioned, with PII-safe response logging and a per-version metrics dashboard.
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
- Design a prompt-registry schema with versions, owners, and environments
- Instrument an LLM-app for per-request observability
- Apply PII scrubbing to LLM request/response logs
- Build a metrics dashboard that catches silent regressions
Program Fit
Where this fits in your program.
Sharpens the same skills your degree expects you to demonstrate.
LLM Application Development
Master · Ai Systems
Strong alignment
This challenge maps to LLM Application Development 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.
- Prompt Versioning
Apply prompt versioning to solve real industry problems and demonstrate production-level capability.
- Observability
Apply observability to solve real industry problems and demonstrate production-level capability.
- Pii Scrubbing
Apply pii scrubbing to solve real industry problems and demonstrate production-level capability.
- Fastapi
Apply fastapi to solve real industry problems and demonstrate production-level capability.
- Streamlit
Apply streamlit to solve real industry problems and demonstrate production-level capability.
- Postgres
Apply postgres to solve real industry problems and demonstrate production-level capability.
Careers
Career paths this challenge builds toward
Completing this challenge demonstrates skills that transfer directly to these roles:
MLOps Engineer
Prompt versioning and observability is core MLOps work for any LLM-powered product team.
This challenge sharpens
- prompt-versioning
- observability
- pii-scrubbing
AI Engineer
Instrumenting an LLM app for observability is what AI engineers ship next at any scaling AI startup.
This challenge sharpens
- fastapi
- observability
- prompt-versioning
Prompt Engineer
A prompt registry and a metrics dashboard are exactly the tooling prompt engineers need to do their job at production scale.
This challenge sharpens
- prompt-versioning
- observability
- pii-scrubbing