An AI take-home assessment is a real-world assignment that a candidate completes on their own time, graded by AI against a fixed rubric — the same criteria applied to every submission. On 100Networks, that grading is AI auto-evaluation with rubric scoring plus knowledge-base fact-checks, across eight assignment types. The result: the richer signal of a take-home, without the reviewer time and inconsistency that usually make take-homes impractical at scale.
Why take-homes — and why they usually break down
A take-home shows you something a live whiteboard can't: how someone works when they have time to think, structure a solution, and polish it — much closer to the actual job. The catch is grading. When 60 candidates submit, manual review is slow, and worse, it's inconsistent: the 50th submission gets read differently than the 1st (fatigue, drift, whoever happens to review it). That inconsistency is unfair to candidates and noisy for you.
Rubric-based AI grading fixes the grading problem, which is what makes take-homes viable as a real screening step instead of a final-round luxury.
Eight assignment types, each with its own rubric
100Networks grades eight assignment types, so a take-home can match the role rather than forcing every candidate through a coding puzzle:
- Coding — implementation tasks
- System design — architecture and trade-off reasoning
- Data analysis — working with a dataset to reach a conclusion
- Case study — structured problem-solving
- Writing — clarity and reasoning in prose
- Design — UX/visual work
- Presentation — communicating an idea
- Mixed — a combination for cross-functional roles
Each type is scored against its own rubric, so a writing sample isn't judged like a coding task.
How the grading actually works
Two things happen when a submission comes in:
- Rubric scoring — the AI evaluates the work against the criteria you'd use yourself (correctness, structure, clarity, completeness — whatever the rubric defines), and produces a score with the reasoning behind it.
- Knowledge-base fact-checks — claims in the submission are checked against a knowledge base, so confidently-wrong answers don't slip through a surface read.
The output isn't a black-box pass/fail. It's a scored evaluation with evidence a human can review in seconds instead of grading from scratch.
The fairness argument (it's the opposite of what people assume)
The common worry is that AI grading is less fair than a human. In practice it's usually more consistent: a rubric applies identical criteria to every candidate, so submission #50 is judged exactly like submission #1. No drift, no fatigue, no "this one happened to land with a tough reviewer." Every candidate is measured against the same bar — which is the definition of a fair process.
And the human stays in charge: the AI produces the score and the evidence; a recruiter or hiring manager makes the decision. This mirrors how AI interviews and Pilot work across 100Networks — AI does the evaluation, a person owns the call.
Where it fits in the funnel
A graded take-home is a strong, skills-based filter that's fairer than a resume keyword scan and lighter than a full interview loop. Combined with AI screening up front and AI interviews after, it gives you a consistent, evidence-backed signal at every stage — and gives your team back the hours that manual grading used to eat.
See how assessments fit with the rest of the platform in the product overview, or read how it all compares to a traditional ATS.