Write up the phrasing eval: 0/15 to 13/15, and what the number hides
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01CGeSZxh1DCtRxmFVSYVGvJ
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# Phrasing evaluation — 31-07-2026
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How Maven words a nudge, measured instead of argued. Counterpart to
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`ROUTING-EVAL-31-07-2026.md`.
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- Fixture + scorer: `internal/phraser/eval/` (`nudges_v1.json`, 15 cases; `eval.go`, `checks.go`)
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- Reproduce: `MAVEN_LLM_URL=http://127.0.0.1:18099 make eval-phrasing`
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- Model: Qwen3.5-0.8B Q4_K_M, the resident model. Not swapped.
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- Commit: `a40bc55` (prompt fix)
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Every check is a string or length test a human can read and disagree with. No model
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grades another model here.
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## Result
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| | before | after |
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|---|---|---|
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| **cases passing every check** | **0/15** | **13/15** |
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| mood in enum | 6/15 | 15/15 |
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| Russian | 2/15 | 14/15 |
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| length (≤120 chars, ≤16 words) | 13/15 | 15/15 |
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| feminine self-reference | 15/15 | 15/15 |
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| no cringe | 13/15 | 15/15 |
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| on topic | 6/15 | 13/15 |
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| p50 latency | 11.4s | 11.4s |
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Latency did not move and is not good. 11s to word one nudge on this box.
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## The bug reproduced
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Yes, exactly as reported. 7 of 15 messages were the literal string `"..."`, and one was
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`"full voice message"`. Both are text copied straight out of the prompt.
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The system prompt said:
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```
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Respond ONLY with valid JSON: {"response": "full voice message", "mood": "neutral"}
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```
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and the user prompt said:
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```
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Respond as JSON: {"response": "...", "mood": "..."}
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```
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A 0.8B does not read `"..."` as "put your answer here". It reads it as the answer. The
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prompt was a worked example whose worked part was blank, so the model filled the slot by
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copying. This is the whole of finding 1.
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## What else was wrong
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Four separate faults, all prompt-side:
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1. **Placeholder echo** (7 cases) — above.
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2. **Wrong language** (13/15 failed the language check). The prompt was entirely English
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and said "in the user's language (Russian or English)". The model picked English. It is
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never English: the nudge is spoken by a Russian piper voice.
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3. **Rule names are English identifiers.** `netdata_critical`, `service_down`, `break` went
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into the prompt raw. The model cannot nudge about a topic it has not been told in words,
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so 9/15 were off topic. The daemon knows what its own rules mean; now it says so.
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4. **Mood invented** (`"warm"`, twice). The enum was listed in a parenthesis at the end of
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an English sentence. Now it is its own line: "ровно одно из: neutral, happy, thinking,
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tired, confused."
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Plus two non-prompt faults the run exposed:
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- **The no-parse fallback was English.** When the model returned nothing usable, the body
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became `fmt.Sprintf("%s — %s", rule, sev)` — `"water — care"` — and that string went to
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a Russian TTS. Now it falls back to plain Russian.
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- **Durations were English.** `humanDur` returns "3 hours"; it was landing verbatim inside
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Russian sentences. Nudges now use a Russian formatter.
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## Three iterations, and what each taught
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| | score | change |
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|---|---|---|
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| baseline | 0/15 | — |
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| iter 1 | 2/15 | Russian prompt, filled-in examples, Russian durations |
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| iter 2 | 11/15 | required keyword per rule, one example instead of five, Russian fallback |
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| iter 3 | **13/15** | examples moved to topics that are not rules |
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The interesting step is 1 → 2. Fixing the placeholder did not fix the disease, it moved it:
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the model stopped copying `"..."` and started copying my first example instead. Five nudges
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in a row came back as `"Ты не пил воду три часа. Налей стакан."` regardless of the rule.
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**A small model copies the nearest concrete text in its prompt.** That is one failure mode
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with two symptoms. The fix that stuck was making the examples about laundry and a laptop
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battery — topics no rule ever produces, so copying them is visible in the score rather than
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invisibly passing the water cases.
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## Do not oversell 13/15
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Seven of the thirteen passes are the **deterministic fallback**, not the model:
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`"Напоминаю: таблетки."`, `"Сервис не отвечает."`, `"Критический алярм: проверь диск."`,
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`"Ты давно не пил воду."`. Those are strings this commit added to Go. The model returned
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nothing parseable and the fallback scored.
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So the honest reading is roughly **6/15 from the model, 7/15 from a fallback, 2/15 failing**.
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The prompt fix is real — `"..."` is nearly gone and the language and mood checks are clean —
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but a large part of the jump is that failure now degrades into Russian instead of into
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`"water — care"`. That is a genuine improvement for the operator and a weak one for the model.
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The two remaining failures: one `"..."` recurrence (`routine-stretch`) and one meal nudge
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that never says food.
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## Broken, found, not fixed
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1. **`checkFeminine` only catches half the constraint.** It scans for masculine
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self-reference and passed 15/15 both runs — but three messages address the *owner* in
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the feminine: "ты давно не отдыхал**а**", "он не ел". The owner is a man. The check has
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no second-person gender test, so this scores clean while being exactly the persona
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failure the constraint exists to prevent. This is the most important gap in the harness.
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2. **Grammar is not checked at all, and it is bad.** `"Он не ел 11 дней"` (it was 11 hours),
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`"Сонуждились 7 дней"` (not a word), `"Они забыли воду"` (wrong person entirely). Every
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one of these passes all six checks. The fixture measures properties, not fluency, and at
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0.8B fluency is the binding constraint.
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3. **Unit confusion.** The model turns hours into days about a third of the time. The
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prompt now says "11 ч"; it reads it as days.
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4. **11s p50.** Unchanged and untouched here. A nudge the model takes eleven seconds to
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word has missed its moment. Worth its own task.
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5. **The keyword hint is close to teaching to the test.** `ruleKeywords` names the word the
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on-topic check looks for. It is defensible — the daemon genuinely knows its rule topics
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and the model genuinely cannot infer them from `netdata_critical` — but the on-topic
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number is softer than the others because of it.
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## Next steps
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1. **Add a second-person gender check** to `checks.go`. Finding 1 above. Until it exists the
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feminine column means less than it looks like.
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2. **Decide whether the fallback should count as a pass.** Right now `Score` cannot tell a
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model answer from a fallback. Either mark fallback bodies in `PhrasedNudge` or count them
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in their own column. Without that, any future prompt change can score well by failing
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more.
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3. **Attack the 11s.** Nudge phrasing is short and non-interactive; thinking off is the first
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thing to try, as it was for routing (#376).
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4. **Re-measure when #122 lands.** The CPT'd Qwen3-1.7B is the target. 13/15 with seven
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fallbacks is the floor it has to beat, and the fluency problems above are the ones a
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bigger, Russian-trained checkpoint should actually fix.
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