CriteriaBotEvaluate Content with Natural Language

CriteriaBot takes a familiar moderation problem and moves it one layer closer to ordinary software logic. Instead of selecting from a fixed catalog of labels such as spam or hate speech, a team writes its own evaluation criteria in plain English. The service then applies those criteria to submitted text and returns a true-or-false result for each one. That makes it less like a single-purpose moderation endpoint and more like a configurable decision layer that can sit inside an application, support queue, or content pipeline.

The approach is practical because many real policies do not fit neatly into universal categories. A gaming community may want to flag spoilers, while a business forum may restrict political discussion. Those are not necessarily problems a generic classifier understands without additional context. CriteriaBot lets an operator describe the boundary directly, then revise it as the community’s expectations become clearer.

Where a programmable evaluator fits

CriteriaBot presents its rules as reusable building blocks. A developer or operations team can create a standard, submit content through the website or API, and use the returned decision to approve, flag, route, or label that content. The same mechanism can be applied to a comment, an email, a support ticket, or text generated by an AI assistant. It is a useful distinction: the product is not promising one universal definition of “bad content”; it is offering a way to encode an organization’s own definition.

• Content moderation: screen for toxicity, harassment, unsafe material, spoilers, or rules specific to a particular community.
• AI application protection: check user input for prompt-injection attempts and inspect model output against safety requirements.
• Brand and policy review: test whether human or AI-written copy matches a preferred voice, internal policy, or compliance rule.
• Classification and routing: sort messages, tickets, or submissions by topic, urgency, eligibility, or another team-defined condition.

There are also plausible uses in spam detection, public-relations monitoring, and data labeling. For example, a small community team could use a rule such as “Does this post contain a direct personal attack?” before sending borderline items to a human moderator. A product team building an LLM assistant could run several checks on incoming prompts and generated replies before displaying them. These workflows do not eliminate review, but they can reduce the repetitive screening work that tends to consume operational time.

How the rules become decisions

The basic workflow is deliberately simple. A user writes or reuses a criterion, sends content through the web interface or API, and receives a boolean judgment. Criteria can be organized and reused, while the service also provides a community library as a source of examples. The API supports synchronous and asynchronous processing, which gives developers a path for both interactive checks and larger batches of text.

Behind the result, CriteriaBot says it combines traditional machine-learning techniques with a panel of smaller open-source large language models. The models vote on how each item matches the stated rule, producing a weighted consensus rather than relying on a single model response. The service also says the panel can access Wikipedia and Wolfram for evaluations that require factual lookup or mathematical reasoning. Public materials do not identify every model, weight, or benchmark, so those claims should be treated as product positioning rather than a substitute for testing on production-like data.

The more interesting feature is the feedback loop. After submitting content, users can provide their own judgment, allowing the system to learn how that organization interprets a criterion. This is especially relevant when a rule sounds clear to a person but has edge cases in practice. A community’s interpretation of harassment, advertising, or political discussion may depend on local norms and previous decisions. Human examples give the evaluator more context than a generic off-the-shelf label can provide.

A useful mental model is “policy as an API”: the rule remains readable by people, while the result can be consumed by code.

That model also explains the main setup challenge. Natural language is easier to write than a custom classifier, but it is not automatically precise. “Is this valuable?” or “Does this feel unsafe?” may produce inconsistent outcomes because the underlying idea is underspecified. Teams should define what counts, what does not count, and how ambiguous cases should be handled. Running a small, representative sample through the system before connecting it to automatic enforcement is a sensible safeguard.

Who should consider CriteriaBot?

CriteriaBot is aimed at people who need custom text decisions but do not want to build a new model for every policy change. Community managers can use it to express local moderation rules. Developers working on LLM products can add checks for prompt injection or unwanted output. Support and operations teams can use criteria to classify incoming requests and send them to the appropriate workflow. The product also leaves room for non-engineers to help define policies, since writing a plain-language rule is generally more accessible than maintaining regular expressions or model-training code.

Its strongest fit is likely a team with changing or highly specific rules. A company that needs only a standard spam filter may find a conventional service easier to evaluate and operate. CriteriaBot becomes more compelling when the requirement sounds like, “Flag content that violates our policy, as our team interprets it,” rather than, “Detect one well-known category.” Developers should also watch for false positives, false negatives, and drift as language or community behavior changes. Human review remains important for high-impact decisions.

Pricing is not publicly listed on the available product page. Users need to register for an API key and should check the official site for current plans. Before committing, a small test batch can reveal whether the rules are understandable, whether results are stable enough for the intended workflow, and how much manual review is still required. That evaluation matters more than a polished demo because custom criteria often behave differently once they meet real-world edge cases.

CriteriaBot’s promise is straightforward: readable rules, API-friendly decisions, and feedback-driven customization. Its flexibility is useful, but teams should validate the service on their own content and keep humans in the loop where an incorrect classification carries serious consequences.