Build a Domain Instruction-Tuning Recipe for a Legal Coach
Overview
What this challenge is about.
Build a Domain Instruction-Tuning Recipe for a Legal Coach. Advanced challenge in code. Writing production code that solves real engineering problems, earn a...
The Brief
What you'll do, and what you'll demonstrate.
Build an instruction-tuning recipe for a legal coach that lifts domain helpfulness without regressing generic capability.
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 multi-source instruction-tuning dataset
- Apply LoRA fine-tuning to a 7B model on a domain dataset
- Evaluate domain lift alongside generic capability regression
- Communicate fine-tuning recipes for internal reuse
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.
- Instruction Tuning
Apply instruction tuning to solve real industry problems and demonstrate production-level capability.
- Lora Fine Tuning
Apply lora fine tuning to solve real industry problems and demonstrate production-level capability.
- Data Curation
Apply data curation to solve real industry problems and demonstrate production-level capability.
- Synthetic Data
Apply synthetic data to solve real industry problems and demonstrate production-level capability.
- Llm Evaluation
Apply llm evaluation to solve real industry problems and demonstrate production-level capability.
- Regression Testing
Apply regression testing 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:
Applied AI Scientist
Owning a domain instruction-tuning recipe end-to-end is the day-to-day of applied AI scientists at vertical-AI startups.
This challenge sharpens
- instruction-tuning
- data-curation
- llm-evaluation
Machine Learning Engineer
Building the training pipeline plus the regression-testing harness is core MLE work for any fine-tuning team.
This challenge sharpens
- lora-fine-tuning
- regression-testing
- instruction-tuning
Data Engineer
Curating, deduplicating, and licensing a multi-source instruction dataset is the data-engineering skillset every fine-tuning team needs.
This challenge sharpens
- data-curation
- synthetic-data
- instruction-tuning