Research Project Brief

AI Research Experiment Brief (Interactive Template)

Interactive template to define small, safe, reproducible AI experiments that produce useful evidence for organizational learning. Guides teams through research questions, scope, data and ethics checks, evaluation, monitoring, success criteria, rollback plans, roles, and documentation for reuse.

Interactive Tool

AI Research Experiment Brief

This interactive brief helps teams design small, safe, and reproducible AI experiments that generate useful evidence for organizational learning. Complete each section with concrete, measurable details. Use the monitoring and rollback fields to reduce operational and ethical risk. Saved briefs can be used as inputs to pilots, approvals, and post-mortems.

State the precise question you want the experiment to answer. Frame it so the outcome is actionable (for example: 'Can a summarization assistant reduce first-draft meeting minutes time by 50% while preserving accuracy?').
Brief context: why this problem matters, who it affects, and any known constraints. Keep this short — link to longer docs if needed.
Write a testable hypothesis. Example: 'Using model X with prompt template Y will improve F1 score on extraction by 10% versus current rule-based approach.'
Define the smallest, realistic scope that can test the hypothesis: target users, sample size, datasets, features, and system boundaries. Avoid wide rollouts.
List required datasets, their formats, sample sizes, ownership, access location, and any labeling needs. Note known quality issues.
Select the checks you have performed or will perform. Provide details below.
Describe outcomes of the checks, identified risks, mitigations, and the person/team responsible for ethics & compliance approvals.
Describe how you will measure outcomes: experimental design (A/B, holdout, before/after), evaluation datasets, metrics, significance thresholds, and how results will be validated by humans.
List one or more concrete, numeric success criteria and the time horizon. Example: 'Increase task completion rate from 65% to ≥75% within 6 weeks' or 'reduce median processing time from 2h to ≤1h'.
Record the current baseline for each metric you will measure so improvements are comparable and verifiable.
These are examples — pick relevant ones and add others in the field below. Monitoring should detect issues early.
List each monitoring metric, its data source, sampling cadence, alert thresholds, and who receives alerts. Include early-warning thresholds and critical thresholds that trigger rollback.
Describe exactly how you will stop or revert the experiment if safety, performance, compliance, or user trust is compromised. Include who has authority to stop, and how to restore prior state.
Identify major risks (operational, ethical, security, legal, reputational) and the mitigations you will put in place. Be explicit about residual risk you will accept.
Who reviews outputs, frequency of review, criteria for escalation, and training required for reviewers. Define the human-in-the-loop workflow.
List the experiment owner, data owner, engineering lead, ethics/compliance contact, and any other roles. Note required compute, labeling support, or budget.
Provide a short timeline with key milestones (e.g., data prep complete, pilot start, evaluation, decision point). Estimate duration in calendar days or weeks.
List approvals required before launch (e.g., legal, security, privacy, business sponsor) and their current status.
Explain how experiment artifacts will be stored and documented so other teams can reproduce results (code, seeds, dataset snapshots, config, evaluation scripts, and a short runbook).
Where will data be stored, who can access it, how long it will be retained, and how it will be securely disposed after the experiment ends?
Estimate direct costs such as labeling, compute, API usage, and contractor time.
Define who decides whether the experiment succeeded, what happens next on success (scale, hand-off) and on failure (retire, iterate, redo with changes).
Use a stable identifier and version string to track this brief and its artifacts.
Links to datasets, notebooks, documentation, previous experiments, or relevant policies.
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