Overview
What this challenge is about.
Build an agentic RAG with context-window budgeting, then evaluate against single-shot RAG. Earn a verifiable certificate.
The scenario
The startup (Seed extension, around 12 staff) is building toward Series A and needs a defensible quality win on long-context workspaces to differentiate from generic assistants.
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.
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