Not everything is a key output: choosing what gets validated at eight a day

OWASP's guidance on LLM misinformation asks for tools and processes that automatically validate key outputs, especially in high-stakes environments. It does not ask you to validate everything. That single word, key, is where a generative pipeline gets designed or neglected. In a system that publishes eight SEO articles every day across seven languages with zero manual intervention, somebody has to name the fields that get checked and the ones that ship unread.

What makes an output key

Start from consequence, not from effort. An output is key when a reader can act on it, or when a rule attaches to it: a figure, a claim about what a product does, a statement about which market or language a page serves. Register, rhythm and headline phrasing are not key by this test. Getting them wrong costs credibility; it does not plant a false belief that someone then repeats.

Fluency is not evidence of correctness. Misinformation, as OWASP defines it, is false or misleading information that appears credible, so the failure you are screening for reads well by construction. Any validation design that leans on output looking finished will pass the exact cases it exists to catch. Checks have to compare the output against something outside it: a retrieved passage, a schema, a locale list, a required marker.

The checks that are never optional

Some fields are key because a rule attaches to them. The AI Act entered into force on 1 August 2024 and became applicable on 2 August 2026. Humans interacting with chatbots should be made aware they are interacting with a machine, and providers of generative AI have to ensure AI-generated content is identifiable. Starting on 2 December 2027, high-risk systems face strict obligations before market entry. Binary conditions like these are cheap to assert and belong in the always-run set.

Pick the grading style from the field

Anthropic's guidance on building evaluations says to structure questions so they allow automated grading: multiple-choice, string match, code-graded or LLM-graded. That maps onto field types directly. A required identifiability marker is a string match. A page's language against its target locale is code-graded. Whether a factual sentence is supported by the passage it was written from needs an LLM grader, which is slower and noisier than the other two.

Resist the pull towards hand review for the awkward fields. The same guidance holds that more questions with slightly lower signal from automated grading beat fewer questions hand-graded by humans, and that most use cases need multidimensional evaluation along several success criteria. At eight publications a day, hand review is not the slower option; it is the absent one.

When nobody reads it before it publishes

Overreliance, in the same OWASP entry, is users placing excessive trust in generated content and failing to verify its accuracy. Read that at the level of the system rather than the person. A platform where manual intervention is 0 and weekly content operations run at roughly 0 h has no human verifier anywhere in the loop, so overreliance stops being a user habit and becomes a property of the architecture.

Attach the checks to the output, not to the model path. One of our platforms publishes articles daily behind three model fallback levels; a fallback changes which writer produced the text, not whether the text was checked. Validation wired into the primary branch leaves the degraded branches unvalidated, which is precisely the moment when output is least predictable.

Grounding shrinks the surface to check

OWASP recommends retrieval-augmented generation to improve reliability by retrieving relevant and verified information from trusted external databases during response generation. The validation consequence matters as much as the quality one. A sentence tied to a retrieved row can be checked by comparison; a free-floating claim needs judgement. Grounding turns expensive graders into cheap ones by giving each claim something concrete to be checked against.

Write down the unchecked list

The honest half of this exercise is the second list. Name the fields that publish without a check and keep that list where the team can read it, because an unwritten exclusion is not a decision; it is a gap nobody can review. When a bad claim does reach a reader, the first useful question is whether that field was on the checked list or the other one.

Treat key as a budget you allocate rather than a quality you detect. Put the binary obligations, identifiability and machine disclosure, in the always-run set because they are cheap to assert. Ground factual claims so the expensive graders have something to compare against. Bind the checks to the output rather than to one model branch. Then record what you chose to leave unchecked, in the same file as what you check.

Where this lands in practice

Sources

OWASP Gen AI Security Project — LLM09:2025 Misinformation — https://genai.owasp.org/llmrisk/llm092025-misinformation/

Claude Platform Docs (Anthropic) — Define success criteria and build evaluations — https://platform.claude.com/docs/en/test-and-evaluate/develop-tests

European Commission (Shaping Europe’s digital future) — AI Act — https://digital-strategy.ec.europa.eu/en/policies/regulatory-framework-ai

Neurolinks case study — Four markets, one codebase — https://neurolinks.be/work/tatano-energy

Neurolinks case study — A personal brand that publishes itself — https://neurolinks.be/work/matthieu-pesesse-media