Overview
What this challenge is about.
Structured-Output Prompts for Invoice Extraction. Intermediate challenge in code. Writing production code that solves real engineering problems, earn a block...
The Brief
What you'll do, and what you'll demonstrate.
Redesign an invoice-extraction prompt with JSON-mode + Schema + targeted few-shot examples to meet a sub-2 percent failure SLA.
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
- Use JSON-mode and JSON Schema to enforce structured LLM outputs
- Mine real failure logs to curate few-shot examples that target known failure modes
- Design an evaluation harness for extraction quality with multiple metrics
- Iterate prompt design under a production SLA
Program Fit
Where this fits in your program.
Sharpens the same skills your degree expects you to demonstrate.
Prompt Engineering
Master · Generative Ai
Strong alignment
This challenge maps to Prompt Engineering 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.
- Structured Output
Apply structured output to solve real industry problems and demonstrate production-level capability.
- Json Schema
Apply json schema to solve real industry problems and demonstrate production-level capability.
- Few Shot Prompting
Apply few shot prompting to solve real industry problems and demonstrate production-level capability.
- Prompt Evaluation
Apply prompt evaluation to solve real industry problems and demonstrate production-level capability.
- Python
Write clean, efficient Python for data processing, automation, and backend services.
- Failure Analysis
Apply failure analysis 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:
Prompt Engineer
Designing a structured-output prompt that meets a production SLA on real customer data is the day-one job of a prompt engineer at any AI product startup.
This challenge sharpens
- structured-output
- json-schema
- few-shot-prompting
AI Engineer
Building the prompt-plus-eval-harness boundary between the model and the product is the AI-engineer skillset hiring managers screen for.
This challenge sharpens
- prompt-evaluation
- python
- failure-analysis
NLP Engineer
Information extraction with field-level accuracy and structured outputs is core NLP-engineer work in B2B AI.
This challenge sharpens
- structured-output
- prompt-evaluation
- failure-analysis