Classification
Show where Classification affected a real decision in the project. Include the implementation, constraint, test or measurement that makes the skill verifiable.
CAREER PROJECT · Intermediate · 2 weekends
Support Ticket Triage Assistant
Classify and route support tickets with confidence thresholds, human review and evaluation.
ClassificationLLMsEvaluationHuman-in-loopAPIs
WHAT THIS PROJECT PROVES
Make the engineering visible.
This brief is designed as portfolio evidence for a AI Engineer path. The goal is not to claim a hiring outcome; it is to make your implementation, judgment and tradeoffs easier to inspect.
LLMs
Show where LLMs affected a real decision in the project. Include the implementation, constraint, test or measurement that makes the skill verifiable.
Evaluation
Show where Evaluation affected a real decision in the project. Include the implementation, constraint, test or measurement that makes the skill verifiable.
Human-in-loop
Show where Human-in-loop affected a real decision in the project. Include the implementation, constraint, test or measurement that makes the skill verifiable.
APIs
Show where APIs affected a real decision in the project. Include the implementation, constraint, test or measurement that makes the skill verifiable.
BUILD PLAN
Finish the core before expanding the scope.
Use this as a project sequence, not a rigid tutorial. Your stack can change; the evidence should still show what you built, why you built it that way and how you know it works.
Define the scope and success criteria
Turn the brief into a small finishable system. Decide what the core Support Ticket Triage Assistant must do, what you will deliberately leave out, and how you will show that it works.
Build the core workflow
Classify and route support tickets with confidence thresholds, human review and evaluation. Keep the first pass focused on the main user or system flow before adding stretch features.
Add engineering proof
Create labeled eval set and triage api. Document the decisions, failure states, tests, performance, security or operational tradeoffs that matter for this project.
Ship and package the evidence
Publish the strongest artifacts, including review queue, error analysis, metrics. Make it easy for a reviewer to understand the problem, architecture, tradeoffs and what you personally implemented.
PORTFOLIO PROOF CHECKLIST
Leave artifacts a reviewer can inspect.
A strong project page should make it possible to understand the system without guessing what you personally built.
01
Labeled eval set
02
Triage API
03
Review queue
04
Error analysis
05
Metrics
06
A concise README explaining the problem, architecture and tradeoffs
07
A working demo, API example or reproducible run path
08
Evidence of testing, monitoring, performance, accessibility or reliability where relevant
RESUME / PORTFOLIO FRAMING
Describe what you actually did.
Built an AI-assisted ticket triage system with confidence thresholds, human review and measured classification quality.
Use this as a starting structure, then replace it with your real implementation details, metrics and constraints. Do not claim outcomes you did not measure.
INTERVIEW TALKING POINTS
Be ready to defend the decisions.
These prompts turn the project from a repository link into a technical conversation about judgment, tradeoffs and evidence.
Why did you choose your approach to Classification, and what alternative did you reject?
Answer with details from your implementation. Prefer concrete constraints, measurements and alternatives over generic best practices.
Where could Support Ticket Triage Assistant fail under real usage, and how would you detect or recover from that failure?
Answer with details from your implementation. Prefer concrete constraints, measurements and alternatives over generic best practices.
How did LLMs affect the rest of the design?
Answer with details from your implementation. Prefer concrete constraints, measurements and alternatives over generic best practices.
What did you measure or test to prove APIs instead of only claiming it?
Answer with details from your implementation. Prefer concrete constraints, measurements and alternatives over generic best practices.
If you had one more week, what would you improve first and why?
Answer with details from your implementation. Prefer concrete constraints, measurements and alternatives over generic best practices.
STRETCH GOALS
Go deeper only after the core is solid.
Active learning
Add it only if it strengthens the engineering story instead of delaying a finished, deployable core.
Drift checks
Add it only if it strengthens the engineering story instead of delaying a finished, deployable core.
Multilingual eval
Add it only if it strengthens the engineering story instead of delaying a finished, deployable core.
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