Overview
What this challenge is about.
Ship a Streaming RAG Endpoint with Caching and Fallbacks. Advanced challenge in code. Writing production code that solves real engineering problems, earn a b...
The Brief
What you'll do, and what you'll demonstrate.
Ship a streaming, cached, fallback-capable RAG endpoint with per-request cost tracking and a clear runbook.
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 Server-Sent Event streaming for LLM responses
- Design a multi-provider fallback chain
- Cache LLM responses safely with the right key composition
- Track and attribute per-request LLM cost
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.
- Llm Api Integration
Apply llm api integration to solve real industry problems and demonstrate production-level capability.
- Streaming
Apply streaming to solve real industry problems and demonstrate production-level capability.
- Response Caching
Apply response caching to solve real industry problems and demonstrate production-level capability.
- Fallback Design
Apply fallback design to solve real industry problems and demonstrate production-level capability.
- Cost Tracking
Apply cost tracking to solve real industry problems and demonstrate production-level capability.
- Fastapi
Apply fastapi 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:
AI Engineer
Shipping a production LLM endpoint with streaming, caching, and fallback is the day-one work of AI engineers at any AI-product startup.
This challenge sharpens
- llm-api-integration
- streaming
- fallback-design
MLOps Engineer
Owning the cost-tracking schema and the runbook bridges directly into MLOps work on inference platforms.
This challenge sharpens
- cost-tracking
- fallback-design
- response-caching
Machine Learning Engineer
The fallback chain plus the local open-weight model brings together application engineering and ML deployment in one project.
This challenge sharpens
- fallback-design
- llm-api-integration
- fastapi