Overview
What this challenge is about.
Agentic RAG with Context-Window Budgeting. Advanced challenge in code. Writing production code that solves real engineering problems, earn a blockchain-verif...
The Brief
What you'll do, and what you'll demonstrate.
Design and evaluate an agentic RAG with a context-window budget controller that meaningfully beats single-shot RAG without runaway cost.
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 an iterative retrieval + summarization agent under a hard token budget
- Compare agentic vs. single-shot RAG fairly on cost and quality
- Apply citation-tracking through multiple agent iterations
- Communicate cost-quality trade-offs in a product memo
Program Fit
Where this fits in your program.
Sharpens the same skills your degree expects you to demonstrate.
Retrieval-Augmented Generation
Master · Ai Systems
Strong alignment
This challenge maps to Retrieval-Augmented Generation 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.
- Agentic Rag
Apply agentic rag to solve real industry problems and demonstrate production-level capability.
- Context Window Management
Apply context window management to solve real industry problems and demonstrate production-level capability.
- Iterative Retrieval
Apply iterative retrieval to solve real industry problems and demonstrate production-level capability.
- Rag Evaluation
Apply rag evaluation to solve real industry problems and demonstrate production-level capability.
- Tool Use
Apply tool use to solve real industry problems and demonstrate production-level capability.
- Python
Write clean, efficient Python for data processing, automation, and backend services.
Careers
Career paths this challenge builds toward
Completing this challenge demonstrates skills that transfer directly to these roles:
AI Engineer
Designing agentic RAG with explicit cost discipline is the kind of practical work AI engineers do at every product-led AI startup.
This challenge sharpens
- agentic-rag
- iterative-retrieval
- tool-use
Machine Learning Engineer
Building a fair quality + cost comparison between agentic and single-shot variants is core MLE work in LLM product teams.
This challenge sharpens
- rag-evaluation
- context-window-management
- python
Prompt Engineer
Designing the per-iteration prompts and summarization templates that keep citations alive is core prompt-engineer territory.
This challenge sharpens
- iterative-retrieval
- tool-use
- context-window-management