Fix the categories, severity boundaries and worked examples for the response types being reviewed.
Human review of AI outputs
Native-speaker review of generated responses against the product policy, with reasons for failures and an escalation path for disputed outputs.
Ask to inspect a sample rubric, calibrated review example, de-identified disagreement record and escalation path before scaling.
Output review decision
What must a reviewer decide before a generated answer reaches a user?
Ordinary output review has a different job from benchmark scoring or adversarial testing: it gives each sampled response a policy disposition the product team can act on.
Calibrate native-speaker reviewers on the languages and dialects the product serves.
Record why an item is ambiguous and send it to adjudication before counting it as a pass.
Return findings by language and failure type with the rubric version used to make the decision.
Policy-led reviewNative-speaker calibrationAdjudication recorded
Human review turns an AI response into a policy decision
Human review of AI outputs checks generated responses against the product policy in the languages where users will read them. Native-speaker reviewers work from a calibrated rubric, record why a response passes or fails, and escalate ambiguous or high-risk cases for adjudication. This is the review and disposition of ordinary output samples. Benchmark scoring belongs in LLM evaluation; deliberately trying to break safety behaviour belongs in AI red teaming. MoniSa scopes the response types, languages, policy categories and correction path with the product team before review volume is set. For generative-AI risk evaluation and measurement: NIST, AI Risk Management Framework: Generative Artificial Intelligence Profile (26 July 2024).
When to use it
When automated scores look healthy but the product team cannot judge whether responses meet policy in every language it serves.
For your team
- AI product quality owner
- Needs policy decisions by language and failure type before deciding whether a release can proceed.
- AI safety owner
- Needs ambiguous and high-risk responses routed to an agreed adjudication owner.
- Locale review lead
- Needs calibrated native-speaker examples rather than an English-only score.
Project requirements and deliverables
What we need from you, how the work is reviewed, and what you receive.
| Typical inputs | Representative responses, target languages and dialects, product policy, risk categories, severity boundaries and worked examples |
|---|---|
| Review path | Reviewer calibration, written category definitions, documented failure reasons, independent escalation and rubric versioning |
| Strongest fit | Ordinary multilingual output review and disposition before or during release; benchmark scoring and adversarial tests have separate paths |
| How the work runs | Agree a pilot rubric, review a representative sample, adjudicate disputed items, then return findings by language and failure type |
What do reviewers need before a pilot?
The product owner and review lead should settle the rubric, language coverage and disputed-item path on a representative pilot. The brief determines the controls; no universal detection or response-time guarantee is implied. For agreement-method interpretation during calibration: Artstein & Poesio, Computational Linguistics, Inter-Coder Agreement for Computational Linguistics (2008).
Scope
Name the response types, languages, policy categories and severity boundaries.
Calibrate
Compare reviewer decisions on worked examples and resolve category disagreement.
Pilot
Review a representative sample before setting a production cadence.
Record
Capture failure reasons and rubric version for findings the product team can act on.
Escalate
Agree who adjudicates ambiguous or high-risk outputs.
Decide
Use the pilot findings to decide the next review scope and acceptance rules.
Ask for review evidence
A sample rubric, calibrated example, de-identified disagreement record and proposed escalation path should be inspected for the languages in the brief. This is a scoping request, not a claim that every response or failure can be found.
Choose the review that matches the product decision
Human review checks ordinary responses against your policy. Benchmark scoring and adversarial testing answer different questions.
LLM evaluation services
Score model behavior against a fixed benchmark and written rubric.
AI red teaming
Probe deliberately for safety failures rather than reviewing ordinary response samples.
AI data services
Scope the broader data and human-review program around the model.
LLM evaluation buyer guide
Compare the evidence to request before placing a review program.
Company-wide language coverage
Availability and specialist fit are confirmed for each project.
110,000+
native linguists and AI data contributors
Founder-reported combined network · 4 Oct 2026
300+
languages across active service lines
4,500+
dialects and regional variants
1,000+
organizations served since 2015
Human Review of AI Outputs questions
When does human AI output review fit?
Use it when ordinary generated responses need a language-aware decision against your product policy. Benchmark scoring belongs in LLM evaluation; deliberate attack testing belongs in AI red teaming.
What do reviewers need before a pilot?
First describe target languages, output type and format, policy categories and severity boundaries. Arrange transfer of real outputs and policy files only after project data-handling rules are agreed.
Does review find every model failure?
No. A scoped sample can reveal and classify issues in the material reviewed; it cannot guarantee that every possible response is safe.
Describe the policy decision the reviewers must make
Tell us the languages, policy categories and sample-output format. Arrange transfer of the policy and outputs after a scoped data-handling path is agreed.
Send a brief