QLoRA Fine-Tune for a Customer-Support Domain Assistant
Overview
What this challenge is about.
Fine-tune a 13B model with QLoRA on support tickets, compare to a RAG baseline, and report trade-offs to earn your verifiable certificate.
The scenario
The scale-up (around 180 people, around 20,000 paying customers across EU and US) sees 12,000 support tickets a month; even a 20% deflection rate from an in-product assistant is worth around USD 40,000 per month in support savings.
The Brief
What you'll do, and what you'll demonstrate.
Compare a QLoRA fine-tuned assistant against a strong RAG baseline on a customer-support task and identify when fine-tuning is worth the maintenance cost.
Earning criteria — what you'll demonstrate
- Run QLoRA fine-tuning on a consumer-class GPU
- Compare fine-tuning to a strong RAG baseline fairly
- Build a rubric-based LLM evaluation for product-shaped outputs
- Reason about the long-term maintenance cost of a fine-tuned model
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.
- Qlora
Apply qlora to solve real industry problems and demonstrate production-level capability.
- Fine Tuning
Apply fine tuning to solve real industry problems and demonstrate production-level capability.
- Rag
Apply rag 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.
- Huggingface
Apply huggingface to solve real industry problems and demonstrate production-level capability.
- Pytorch
Apply pytorch 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:
AI Engineer
Comparing fine-tuning vs. RAG on a real product task and writing the maintenance-cost memo is exactly the day-one work of an AI engineer at any B2B SaaS deploying LLMs.
This challenge sharpens
- qlora
- rag
- fine-tuning
Machine Learning Engineer
Running QLoRA training on constrained GPUs and reporting honest evaluation results is core MLE work for any LLM team.
This challenge sharpens
- qlora
- pytorch
- huggingface
NLP Engineer
Designing rubric-based evaluation for product-shaped LLM outputs is the NLP-engineer skillset for support and assistant products.
This challenge sharpens
- llm-evaluation
- fine-tuning
- rag