> ## Documentation Index
> Fetch the complete documentation index at: https://docs.hellorecruiter.ai/llms.txt
> Use this file to discover all available pages before exploring further.
# Setting Up a Take-Home Assessment
> Add an AI-evaluated take-home coding assessment to a job, steer its focus and rubric, and review what candidates submit.
**Who is this for?** Users with **Administrator** and **Recruiter** roles.
A take-home assessment is a coding task a candidate completes on their own time, before an interview. The AI writes each candidate's problem statement, evaluates their submission once they upload it, and builds a scorecard for you to review — you don't grade anything by hand.
Want candidates to solve the problem with a provided AI coding assistant instead of entirely on their own? See [AI-Assisted Coding](/companies/jobs/ai-assisted-coding) — an optional add-on to the round described below, with a video walkthrough of the feature.
## Adding a Take-Home Round
From the job's interview workflow, add a new round.
Select the take-home assessment round type.
Configure the **deadline** and **Interview Guidance Mode**. If [AI-Assisted Coding](/companies/jobs/ai-assisted-coding) is available on your account, you'll also see its toggle here.
The deadline is currently fixed at **5 days** and isn't configurable per job. Resending an invitation resets it to a fresh 5-day window — it doesn't preserve the original one.
## Auto vs Guided Mode
Every take-home round has an **Interview Guidance Mode** with two options.
### Auto
Leave this on and the AI builds the whole assessment for you — it writes the problem statement and the evaluation rubric directly from the job description and the evaluation criteria you've already set up. No extra input needed.
### Guided
Switch to Guided and a free-text box appears for direction. Whatever you type becomes the AI's top-priority instruction — it reshapes the scenario, the rubric, and what the candidate is asked to submit.
A few things worth keeping in mind when you write guidance:
* Call out the specific skill you care about most for the role, not just the general subject area
* Add any expectations that go beyond "does it run," if they matter for this role
* Reinforce seniority expectations if you want a different bar than what the AI infers from the job posting
Your guidance text is the strongest input into both the scenario and the rubric — stronger than the job description, which the AI only uses as background context.
## Every Candidate Gets a Unique Problem
* **Every candidate gets a different problem.** The AI writes a fresh scenario for each candidate applying to the same job, so candidates can't share or look up each other's answers.
* **But the bar stays the same for everyone.** The difficulty, the rubric, and what candidates need to submit stay identical across candidates for the same job — only the scenario or story around the problem changes.
## Shaping the Evaluation Rubric
The AI automatically builds an evaluation rubric for the take-home — you never have to write one from scratch. It's grouped into a small set of candidate-facing categories, generated from your evaluation criteria and your Guided-mode text.
There's no separate screen where you hand-edit these rubric groups directly. Instead, you shape the rubric in two ways:
1. **Your Guided-mode text** is the strongest lever — it shifts what the whole rubric weighs most heavily.
2. **Per-criterion instructions on the job's Evaluation tab.** Each criterion has its own free-text field, **"Guidance for Hello AI,"** treated as a strict, must-follow instruction when scoring that specific criterion. See [Evaluation Criteria](/companies/jobs/evaluation-criteria) for more on setting these up.
## What the Candidate Experiences
Candidates get an email invitation with a link, a deadline, and instructions — you don't need to walk them through it. They read the problem statement, solve it on their own machine, and upload a ZIP of their work.
See the [Take-Home Assessment](/candidates-guide/take-home-assessment) candidate guide for the full walkthrough.
## Reviewing a Submission
Once a candidate submits, open their profile and go to the **Interview** tab for the take-home round. You'll see an **Assessment Analysis** section with:
* **Problem Statement** — the task the candidate was given
* **Submission Files** — their submitted files, with AI-highlighted evidence pinned to relevant code
You'll also see the same requirement match, risks and concerns, and skill proficiencies sections shared by every interview round type, on the candidate's **Overview** tab.
If a candidate includes audio or video in their submission (a recorded pitch, for example), it's stored and viewable on their profile, but the AI doesn't analyze its content — only what's in their written files factors into the score.
## Related Guides