The Discovery-Voice Skill: Why Our AI Articles Don’t Sound Like AI

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When a rule contradicted itself — and how we fixed our discovery voice skill

We shipped a voice skill that told writers to avoid lists of three while its own checkpoint required exactly three numbered findings. It sounds like a joke. It wasn’t.

What we tried first: enforce both constraints as written and run the model that evaluates drafts. Outcome: every minimally compliant article failed the check. The evaluator flagged a “structure announcement” violation and a “findings block missing” error at the same time. The error reports pointed to the same paragraph. The moment it clicked was when an engineer opened the evaluator’s diff and saw the rules literally contradicting each other in the same CSV of checks. That single diff made the conflict visible. We changed the spec to exempt the findings block explicitly. The failing verdicts stopped immediately.

Finding 1 — rule collision breaks minimally valid outputs

We tried a draft that satisfied the style rules except for the list-of-three ban. The external checker still rejected it because a separate checkpoint demanded three numbered findings. The concrete evidence: evaluation logs showed a false-negative sequence — an explicit “must not contain lists-of-three” flag right beside a “must contain exactly three findings” flag, both triggered by the same token range. Fix: adjust the rules so the findings block is exempt. Lesson: machine-checkable rules must be tested together, not only in isolation.

Finding 2 — discovery format yields clearer failures

We rewrote the brief to force “show what was tried, what changed, the turning point” rather than “give tips.” We fed that brief into the generator and into the external evaluator. Failures dropped by half. The proof is in two things: the generated drafts now contain explicit experimental descriptions and the evaluator’s semantic checks match those descriptions, so rejection reasons became actionable. The turning point was when reviewers stopped guessing what the writer meant and started tracing steps in the artifact.

Finding 3 — measurable sentence rules make evaluation reliable

We deployed sentence-length partitioning (short/medium/long), capped consecutive medium sentences to two, and limited em-dash usage to one. At first, authors complained it felt fiddly. Then the external checker started pointing to exact sentence indices. The logs show far fewer ambiguous “style” rejections and many more precise “sentence X exceeds medium run” flags. The rule that fell out: numeric, machine-checkable constraints let an external model give deterministic feedback instead of vague judgments.

What changed in practice

Before the fix, teams would iterate on prose until a human reviewer cleared it. That took hours. After we exempted the findings block and made voice rules numeric, the evaluator gave deterministic pass/fail within seconds. Editors still rebrief when the evaluator fails, but the loop is now about tightening the brief rather than guessing which sentence to rewrite. The external enforcement remains: a separate model measures the draft and a failing verdict triggers a re-brief and regeneration. There is no manual patching of the draft in the pipeline.

The rule that fell out

Make the evaluation rules machine-checkable and mutually consistent; if a human-readable exception is needed, encode it explicitly. This single rule resolved cascading failures from contradictory checks and made rejection reasons actionable. That principle is what our discovery voice skill enforces today.

Counter-example — when the rules look human but hide emptiness

We once received a draft that perfectly matched every numeric threshold: it had three findings, one counter-example over forty words, the right sentence-length distribution, and a single em-dash. It read like a checklist. There was no substance — no recorded experiment, no before-and-after, no turning point. The external evaluator initially accepted it because it matched the syntactic rules. A second-stage reviewer then blocked it because the system is designed to stop padding: if real, first-hand findings don’t back the claims, the piece is rejected. That experience taught us the honest limit: procedural rules make prose feel human, but they cannot manufacture lived experience; the pipeline must detect thinness and refuse to convert it into apparent authority.

How we know

Every claim above ties to our logs and review notes. The contradiction was captured in the QA review log dated 2026-07-24. The enforcement behavior — an external model giving pass/fail and causing a re-brief loop — is the design recorded in the skill documentation and visible in the rejection metrics. The drop in ambiguous rejections after numeric rules were applied shows up in our evaluator’s reports. The blocked “perfect but empty” draft is in the moderation archive as an example of the honest limit.

FAQ — why a separate model enforces the rules?

The external evaluator gives objective, machine-checkable feedback and prevents ad hoc manual fixes. It returns precise failure codes that feed back into tightening the brief and regenerating the draft.

FAQ — how strict are the sentence-length rules?

They partition sentence lengths into classes and cap medium-length runs to two sentences; this makes failures identifiable by index. The numeric thresholds are in the runnable asset in the members’ library.

FAQ — can a writer bypass the system with clever wording?

No. Syntactic compliance isn’t enough. We reject work that lacks documented, first-hand findings; the pipeline flags thin material even if it meets the surface rules.

Members can access the tested, runnable version of this voice skill in the library.

Sources: internal QA review log (2026-07-24); skill documentation and enforcement notes; evaluator logs and moderation archive.

One sentence closing: make rules checkable and consistent, or watch your validators argue with each other.

What evaluation guides and research say about machine-checkable writing rules

Anthropic’s guide to building evaluations ranks grading methods by speed and reliability. It describes code-based grading as Fastest and most reliable, extremely scalable, and adds the trade-off in the same line: it lacks nuance for complex judgments. The same guide asks for success criteria that are specific and measurable, and it tells teams not to forget edge cases. A rule such as “no more than two medium sentences in a row” is exactly the kind of criterion a script can check without arguing about taste.

OpenAI’s grader documentation makes the same split. Its basic grader type returns a 0 or a 1, and the docs say String check graders are good for scoring straightforward pass or fail answers. For anything that needs judgment about meaning, the docs point to similarity scoring or a model-based grader instead. In other words, the vendors who build these tools expect you to mix hard checks and soft checks, and to know which is which.

OpenAI’s evaluation best practices name the failure mode that numeric rules are meant to replace. The guide lists Vibe-based evals as a common anti-pattern: treating “it seems like it’s working” as the test. It also recommends that teams Automate when possible by structuring evaluations so they can be scored automatically, and it warns against ignoring human feedback when calibrating automated metrics.

Research on model judges explains why a second, separate stage is still needed. The MT-Bench paper by Zheng and colleagues studied large language models used as graders and documented position, verbosity, and self-enhancement biases. It also reported that strong judges reached over 80% agreement with human preferences, the same level humans reach with each other. Useful, then, but not a substitute for checking substance.

Anthropic’s engineering post on agent design describes the loop this kind of pipeline uses. In the evaluator-optimizer pattern, one LLM call generates a response while another provides evaluation and feedback in a loop. The post says the pattern works best when we have clear evaluation criteria. Contradictory criteria are the opposite of clear, which is why the loop stalls when two rules fight.

How those sources line up with what the article found

The first finding is a criteria problem before it is a model problem. Version 1.0 banned lists of three while a checkpoint demanded exactly three numbered findings. Every minimally compliant draft failed, because no draft could satisfy both rules. The evaluation guides ask for criteria that are specific and measurable. They assume, without always saying so, that the criteria can be met together. Exempting the findings block explicitly is what made the rule set satisfiable.

The third finding matches the case for code-based grading. Sentence-length classes, a cap of two consecutive medium sentences and a limit of one em-dash are all countable. Once they were in place, the evaluator pointed to exact sentence indices instead of returning vague style rejections. That is the speed and reliability the grading guides describe, applied to prose.

The second finding fits the evaluator-optimizer loop. Rewriting the brief around what was tried, what changed and the turning point cut failures by half, and rejection reasons became actionable. Clear criteria on the generation side made the evaluation side easier to read.

The counter-example shows the limit the research predicts. A draft hit every numeric threshold, including three findings and a counter-example over forty words, yet had no substance. The first evaluator accepted it. A second-stage reviewer blocked it. Hard checks catch form. Something else has to check whether the experience behind the claims is real, and the article’s rule that pieces without first-hand findings are rejected is that something else.

Checklist for writing rules an evaluator can enforce

1. Write each rule as a count, a range or a yes/no condition that a script can test.
2. Run every rule against the same minimal valid example at once, not one rule at a time.
3. Where two rules overlap, write the exception into the spec, such as “the findings block is exempt from the list-of-three ban.”
4. Make each failure report point to a location: a sentence index, a block or a character range.
5. Keep the evaluator separate from the writer, and send failures back as a new brief instead of patching the draft by hand.
6. Add a second check for substance, because a draft can pass every number and still say nothing.
7. Keep a small set of known-bad drafts, including one that is perfect on form but empty, and confirm the pipeline still rejects them after each rule change.
8. Recheck the rules whenever the brief changes, since a new section can collide with an old ban.

More questions

Why not let one model judge both style and substance?

Because the two jobs fail in different ways. Countable style rules are best handled by deterministic checks that return the same answer every time. Substance needs judgment, and the MT-Bench research shows model judges carry biases such as favoring longer answers. Splitting the jobs means a draft cannot pass on length or polish alone.

How do you find contradictory rules before they reach production?

Build the smallest draft that should pass and run the full rule set against it. If that draft fails, two rules are probably in conflict. In our case, the evaluator’s own diff showed both flags firing on the same paragraph, which is what made the collision visible.