Overview
What this challenge is about.
Multi-Turn Dialogue Manager for a Banking Assistant. Advanced challenge in code. Writing production code that solves real engineering problems, earn a blockc...
The Brief
What you'll do, and what you'll demonstrate.
Build a multi-turn dialogue manager with explicit state tracking and escalation that meets accuracy and task-success targets.
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 hybrid state-machine + LLM dialogue manager
- Implement and evaluate intent classification and slot filling
- Manage multi-turn context and graceful escalation
- Communicate dialogue-policy choices to a product team
Program Fit
Where this fits in your program.
Sharpens the same skills your degree expects you to demonstrate.
Aligned coursework coming soon.
Skills
Skills you'll demonstrate.
Each one shows up on your verified credential.
- Dialogue Management
Apply dialogue management to solve real industry problems and demonstrate production-level capability.
- Intent Classification
Apply intent classification to solve real industry problems and demonstrate production-level capability.
- Slot Filling
Apply slot filling to solve real industry problems and demonstrate production-level capability.
- State Tracking
Apply state tracking to solve real industry problems and demonstrate production-level capability.
- Evaluation
Apply 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.
Careers
Career paths this challenge builds toward
Completing this challenge demonstrates skills that transfer directly to these roles:
NLP Engineer
Building a hybrid state-machine + LLM dialogue manager that meets task-success targets is the bread-and-butter NLP-engineer work at any consumer chatbot team.
This challenge sharpens
- dialogue-management
- intent-classification
- slot-filling
AI Engineer
Wiring tool calls into a dialogue policy with evaluation is core AI-engineer work in product orgs.
This challenge sharpens
- tool-use
- evaluation
- state-tracking
Machine Learning Engineer
Owning intent classifier quality plus end-to-end task evaluation is the discipline MLEs bring to conversational systems.
This challenge sharpens
- intent-classification
- evaluation
- state-tracking