diff --git a/app/llm/med_qa.py b/app/llm/med_qa.py
new file mode 100644
index 0000000..45e4895
--- /dev/null
+++ b/app/llm/med_qa.py
@@ -0,0 +1,248 @@
+"""Answers to patient questions about their drug — ONLY from the ГРЛС instruction excerpt (SPEC §7 п.5).
+
+Pipeline (deterministic except for the optional rephrasing step):
+
+1. **Dose guard.** Any question about the dose ("можно увеличить дозу?") is never answered by the bot:
+   the personal dose is the doctor's decision -> `None` (caller says «уточните у врача»).
+2. **Topic.** An explicit `topic` from the engine/rules wins (e.g. `quit_intent` -> "onset"); otherwise a
+   keyword classifier over the scrubbed question picks one of the supported section codes.
+3. **Section.** The matching `DrugSection` is fetched via `integrations.drug_info` (lane B fills real
+   ГРЛС excerpts). No section for the topic -> `None`.
+4. **Sentences.** The section is split into sentences; sentences with dosing regimens are dropped (we never
+   quote "до 200 мг" to a patient); the rest are scored by keyword overlap with the question, and the top
+   ones (kept in original order) form the excerpt. Best score 0 -> the excerpt has no answer -> `None`.
+5. **Rephrasing.** The excerpt goes to `gateway.phrase("med_answer", ...)` with the excerpt as the base
+   meaning, so the gateway's forbidden-output filter and template fallback apply. On top of that we reject
+   rephrasings that add content not present in the excerpt (new numbers, mostly new words) and fall back to
+   the excerpt itself.
+6. **Framing.** An intro ("По инструкции к препарату:") and a fixed doctor note from `config/texts/med_qa.yaml`
+   are added outside the LLM, so they are always present.
+
+Logs never contain the question or the answer text — only codes.
+"""
+
+import logging
+import re
+from functools import lru_cache
+from pathlib import Path
+
+import yaml
+from pydantic import BaseModel
+
+from app.config import get_settings
+from app.core.types import ScrubbedText
+from app.integrations.base import DrugInfo, DrugSection, integrations
+from app.llm.gateway import get_gateway, is_forbidden
+
+log = logging.getLogger(__name__)
+
+PHRASE_INTENT = "med_answer"
+MAX_SENTENCES = 3  # the answer is meant to be 2–3 short sentences
+MAX_EXCERPT_CHARS = 350
+# Share of content-word stems in an LLM rephrasing that may be absent from the excerpt (and the question).
+# Simplifying a sentence legitimately introduces some new words; a mostly new text means the model wrote
+# its own answer instead of rephrasing the instruction, which SPEC §7 п.5 forbids.
+MAX_NOVEL_STEM_SHARE = 0.5
+STEM_LEN = 5
+
+SECTIONS = ("side_effects", "onset", "alcohol", "discontinuation", "administration", "interactions", "overdose")
+# Engine/rule codes accepted as `topic` (demo days 2, 5, 10) -> section code.
+TOPIC_ALIASES = {
+    "nausea": "side_effects",
+    "side_effect": "side_effects",
+    "quit_intent": "onset",
+    "alcohol_interaction": "alcohol",
+}
+
+
+def _rx(*patterns: str) -> re.Pattern[str]:
+    return re.compile("|".join(patterns), re.IGNORECASE)
+
+
+# Question -> topic. Order matters: the first match wins. Specific topics go first; "onset" precedes
+# "discontinuation" because in the demo "от таблеток только хуже, брошу" is driven by the (delayed) effect.
+TOPIC_CUES: tuple[tuple[str, re.Pattern[str]], ...] = (
+    ("side_effects", _rx(r"тошн", r"ташн", r"мутит", r"рвот", r"побочк", r"побочн")),
+    ("alcohol", _rx(r"алкогол", r"\bпив", r"выпи(ть|л|ва)", r"\bвин(о|а|ом)\b", r"водк", r"спиртн")),
+    ("overdose", _rx(r"передоз", r"лишн\w* таблет", r"две таблетки сразу", r"больше,? чем назнач")),
+    ("interactions", _rx(r"совмест", r"вместе с", r"други\w* (лекарств|препарат|таблет)", r"сочета")),
+    (
+        "onset",
+        _rx(r"подейств", r"когда (начн|будет|станет)", r"не помога", r"не действ", r"эффект", r"(только|стало) хуже"),
+    ),
+    ("discontinuation", _rx(r"брос", r"отмен", r"переста\w*", r"прекрат", r"отказ\w* от")),
+    ("administration", _rx(r"как (правильно )?(принимать|пить)", r"(до|после|во время) еды", r"натощак")),
+)
+
+# Questions about the dose are always redirected to the doctor, whatever the instruction says.
+DOSE_QUESTION = _rx(r"доз", r"\d+\s*мг", r"полтаблет", r"половин\w* таблет", r"(увелич|уменьш|сниз)\w* (приём|прием)")
+
+# Sentences quoting a dosing regimen are never shown: the patient's dose is in the prescription, set by
+# the doctor, and an instruction range ("до 200 мг/сут") read out of context reads like advice.
+DOSING_SENTENCE = _rx(r"\d+\s*мг", r"доз\w*\s+(может|можно|следует|увелич|сниж|повыш|уменьш)", r"мг/сут")
+
+# Section-level cues: a sentence that talks about the section's subject gets +1 even without word overlap,
+# so an explicit topic with a vague question ("брошу") still finds e.g. the onset sentence.
+SECTION_CUES: dict[str, re.Pattern[str]] = {
+    "side_effects": _rx(r"побочн", r"нежелательн", r"проход"),
+    "onset": _rx(r"эффект", r"недел", r"улучшени"),
+    "alcohol": _rx(r"алкогол", r"этанол", r"спиртн"),
+    "discontinuation": _rx(r"отмен", r"прекращ", r"синдром"),
+    "administration": _rx(r"еды", r"сутки", r"принима"),
+    "interactions": _rx(r"одновременн", r"совместн", r"ингибитор"),
+    "overdose": _rx(r"передозиров"),
+}
+
+# Symptom groups: prefixed verbs ("подташнивает") defeat prefix stemming, so a symptom named in the
+# question is matched against sentences by group (+2 — the strongest relevance signal).
+SYMPTOM_GROUPS: tuple[re.Pattern[str], ...] = (
+    _rx(r"тошн", r"ташн", r"мутит", r"рвот"),
+    _rx(r"\bсон\b", r"\bсна\b", r"бессонн", r"засып", r"уснуть", r"сонлив"),
+    _rx(r"головн\w* бол", r"голова болит", r"болит голова"),
+    _rx(r"головокруж", r"кружится"),
+    _rx(r"понос", r"диаре", r"желуд", r"живот"),
+    _rx(r"либидо", r"влечени", r"потенци", r"эякуляц"),
+    _rx(r"тревог", r"беспокойств", r"возбужден"),
+    _rx(r"пот(ливость|еет|ею)", r"потоотдел"),
+)
+
+_SENTENCE_SPLIT = re.compile(r"(?<=[.!?…])\s+")
+_WORD = re.compile(r"[а-яёa-z]+", re.IGNORECASE)
+_NUMBER = re.compile(r"\d+")
+
+
+class MedAnswer(BaseModel):
+    text: str  # patient-facing: intro + (rephrased) excerpt + doctor note
+    section: str  # DrugSection.section code
+    source: str  # ГРЛС reference from DrugSection.source
+    phrased_by_llm: bool  # False when the excerpt is shown verbatim (mock, rejection, failure)
+
+
+class MedQATexts(BaseModel):
+    fallback: str
+    intro: str
+    doctor_note: str
+
+
+@lru_cache
+def load_med_qa_texts(path: Path | None = None) -> MedQATexts:
+    path = path or get_settings().config_dir / "texts" / "med_qa.yaml"
+    return MedQATexts.model_validate(yaml.safe_load(path.read_text(encoding="utf-8")))
+
+
+def fallback_text() -> str:
+    """«Уточните у врача» — what the caller sends when `med_qa` returns `None`."""
+    return load_med_qa_texts().fallback
+
+
+def _norm(text: str) -> str:
+    return " ".join(text.lower().replace("ё", "е").split())
+
+
+def _stems(text: str) -> set[str]:
+    # Crude Russian stemming: first STEM_LEN letters of words long enough to carry meaning (skips "не", "с").
+    return {w[:STEM_LEN] for w in _WORD.findall(_norm(text)) if len(w) >= 4}
+
+
+def detect_topic(question: str) -> str | None:
+    text = _norm(question)
+    for topic, pattern in TOPIC_CUES:
+        if pattern.search(text):
+            return topic
+    return None
+
+
+def _score(sentence: str, question: str, q_stems: set[str], section: str) -> int:
+    s = _norm(sentence)
+    score = sum(2 for g in SYMPTOM_GROUPS if g.search(question) and g.search(s))
+    cue = SECTION_CUES.get(section)
+    if cue is not None and cue.search(s):
+        score += 1
+    return score + len(q_stems & _stems(s))
+
+
+def select_excerpt(section: DrugSection, question: str) -> str | None:
+    """Most relevant sentences of the section (original order), or `None` if nothing is relevant."""
+    q = _norm(question)
+    q_stems = _stems(q)
+    sentences = [s.strip() for s in _SENTENCE_SPLIT.split(section.text.strip()) if s.strip()]
+    scored = [
+        (_score(s, q, q_stems, section.section), i, s)
+        for i, s in enumerate(sentences)
+        # Defence in depth: never surface a sentence the gateway would block if an LLM had written it.
+        if not DOSING_SENTENCE.search(s) and not is_forbidden(s)
+    ]
+    relevant = [x for x in scored if x[0] > 0]
+    if not relevant:
+        return None
+    # Highest score first, ties by position; then restore reading order of the chosen sentences.
+    top = sorted(sorted(relevant, key=lambda x: (-x[0], x[1]))[:MAX_SENTENCES], key=lambda x: x[1])
+    return _trim(" ".join(s for _, _, s in top))
+
+
+def _trim(text: str) -> str:
+    if len(text) <= MAX_EXCERPT_CHARS:
+        return text
+    cut = text[:MAX_EXCERPT_CHARS].rsplit(" ", 1)[0].rstrip(",;:")
+    return cut + "…"
+
+
+def adds_content(answer: str, excerpt: str, question: str) -> bool:
+    """True if a rephrasing introduces facts absent from the excerpt (numbers, mostly new words)."""
+    if not set(_NUMBER.findall(answer)) <= set(_NUMBER.findall(excerpt)):
+        return True  # new numbers = new doses/terms/frequencies — never allowed
+    stems = _stems(answer)
+    if not stems:
+        return True
+    novel = stems - _stems(excerpt) - _stems(question)
+    return len(novel) / len(stems) > MAX_NOVEL_STEM_SHARE
+
+
+async def _rephrase(excerpt: str, question: str, inn: str, topic: str) -> tuple[str, bool]:
+    text = await get_gateway().phrase(PHRASE_INTENT, {"excerpt": excerpt, "question": question, "drug": inn})
+    # The gateway returns the base (== excerpt, whitespace-normalised) on failure/filter; mock echoes it.
+    if " ".join(text.split()) == " ".join(excerpt.split()):
+        return excerpt, False
+    if adds_content(text, excerpt, question):
+        log.warning("med_qa rephrasing rejected: adds content topic=%s", topic)
+        return excerpt, False
+    return text, True
+
+
+async def med_qa(
+    inn: str,
+    question: ScrubbedText,
+    *,
+    topic: str | None = None,
+    drug_info: DrugInfo | None = None,
+) -> MedAnswer | None:
+    """Answer about drug `inn` strictly from its ГРЛС excerpt; `None` -> caller sends `fallback_text()`."""
+    if DOSE_QUESTION.search(_norm(question)):
+        log.info("med_qa: dose question redirected to doctor")
+        return None
+    explicit = TOPIC_ALIASES.get(topic, topic) if topic else None
+    resolved = explicit if explicit in SECTIONS else detect_topic(question)
+    if resolved is None:
+        log.info("med_qa: topic not detected")
+        return None
+    try:
+        sections = await (drug_info or integrations.drug_info).sections(inn)
+    except Exception as exc:
+        log.warning("med_qa: drug_info failed error=%s", type(exc).__name__)
+        return None
+    section = next((s for s in sections if s.section == resolved), None)
+    if section is None:
+        log.info("med_qa: no section topic=%s", resolved)
+        return None
+    excerpt = select_excerpt(section, question)
+    if excerpt is None:
+        log.info("med_qa: no relevant sentence topic=%s", resolved)
+        return None
+    body, by_llm = await _rephrase(excerpt, question, inn, resolved)
+    texts = load_med_qa_texts()
+    return MedAnswer(
+        text=f"{texts.intro} {body}\n\n{texts.doctor_note}",
+        section=section.section,
+        source=section.source,
+        phrased_by_llm=by_llm,
+    )
diff --git a/config/texts/med_qa.yaml b/config/texts/med_qa.yaml
new file mode 100644
index 0000000..8c05100
--- /dev/null
+++ b/config/texts/med_qa.yaml
@@ -0,0 +1,12 @@
+# Тексты ответов о препарате (med_qa, SPEC §7 п.5). Зона clinical вычитывает формулировки.
+# Сам ответ берётся ТОЛЬКО из выдержки инструкции ГРЛС; здесь — лишь обрамление и запасной текст.
+# Правила: без советов по дозам, без рекомендаций препаратов, решение об изменениях — за врачом.
+
+# Если в инструкции нет ответа (нет раздела / нет подходящего фрагмента / вопрос о дозе).
+fallback: "В инструкции к препарату нет ответа на этот вопрос. Уточните, пожалуйста, у вашего врача."
+
+# Вступление перед выдержкой — пациент должен понимать, что это текст инструкции, а не совет бота.
+intro: "По инструкции к препарату:"
+
+# Всегда добавляется в конце ответа (не проходит через LLM).
+doctor_note: "Не меняйте схему приёма и не прекращайте его самостоятельно — любые изменения решает ваш врач."
diff --git a/config/texts/phrases.yaml b/config/texts/phrases.yaml
index 4ab20bc..111058b 100644
--- a/config/texts/phrases.yaml
+++ b/config/texts/phrases.yaml
@@ -19,6 +19,8 @@ phrases:
   sleep_support: "Спасибо, что рассказали про сон. Я отмечу это, и ваш врач увидит, как вы спите."
   book_visit_offer: "Предлагаю записаться к врачу пораньше. Выбрать удобное время можно кнопкой ниже."
   doctor_notified: "Я передал информацию вашему врачу. Он свяжется с вами."
+  # med_qa: базовый смысл = выдержка из инструкции ГРЛС; LLM только упрощает её, ничего не добавляя.
+  med_answer: "{excerpt}"
 
 # Шаблон саммари для врача (только по структурированным данным).
 summary:
diff --git a/tests/test_llm_med_qa.py b/tests/test_llm_med_qa.py
new file mode 100644
index 0000000..03b0624
--- /dev/null
+++ b/tests/test_llm_med_qa.py
@@ -0,0 +1,229 @@
+"""med_qa: answers about the drug only from the instruction excerpt (SPEC §7 п.5), demo days 2/5/10."""
+
+import pytest
+
+from app.core.types import ScrubbedText
+from app.integrations.base import DrugSection
+from app.llm import med_qa as med_qa_module
+from app.llm.gateway import LLMGateway, is_forbidden
+from app.llm.med_qa import MedAnswer, adds_content, detect_topic, fallback_text, med_qa, select_excerpt
+from app.llm.mock import MockTasks
+
+S = ScrubbedText
+SRC = "ГРЛС: тестовая выдержка (синтетика)"
+
+# Synthetic, simplified test excerpts — NOT real ГРЛС data (lane B owns data/meds/grls).
+FAKE_SERTRALINE = {
+    "side_effects": (
+        "Наиболее частые нежелательные реакции в начале терапии: тошнота, диарея, сухость во рту. "
+        "Тошнота обычно возникает в первые дни приёма и проходит в течение 1–2 недель. "
+        "Для уменьшения тошноты препарат принимают после еды. "
+        "Возможны нарушения сна и головная боль."
+    ),
+    "onset": (
+        "Терапевтический эффект развивается постепенно. "
+        "Улучшение обычно отмечается через 2–4 недели регулярного приёма. "
+        "Суточная доза может увеличиваться врачом до 200 мг."
+    ),
+    "alcohol": (
+        "Совместный приём с алкоголем не рекомендуется. "
+        "Алкоголь может ухудшать сон и настроение и усиливать побочные эффекты."
+    ),
+    "discontinuation": (
+        "Резкое прекращение приёма может вызвать синдром отмены: головокружение, тревогу, нарушения сна. "
+        "Отмену проводят постепенно под контролем врача."
+    ),
+}
+
+
+class FakeDrugInfo:
+    def __init__(self, sections: dict[str, str] | None = None) -> None:
+        self._sections = FAKE_SERTRALINE if sections is None else sections
+        self.calls: list[str] = []
+
+    async def sections(self, inn: str) -> list[DrugSection]:
+        self.calls.append(inn)
+        if inn != "сертралин":
+            return []
+        return [DrugSection(inn=inn, section=k, text=v, source=SRC) for k, v in self._sections.items()]
+
+
+class BrokenDrugInfo:
+    async def sections(self, inn: str) -> list[DrugSection]:
+        raise RuntimeError("grls down")
+
+
+class FixedPhraseTasks(MockTasks):
+    """Simulates an LLM that returns a fixed rephrasing."""
+
+    name = "fake-llm"
+
+    def __init__(self, reply: str) -> None:
+        self.reply = reply
+
+    async def phrase(self, intent: str, payload: ScrubbedText, base_text: str) -> str | None:
+        return self.reply
+
+
+@pytest.fixture
+def info() -> FakeDrugInfo:
+    return FakeDrugInfo()
+
+
+@pytest.fixture
+def mock_gateway(monkeypatch: pytest.MonkeyPatch) -> None:
+    monkeypatch.setattr(med_qa_module, "get_gateway", lambda: LLMGateway(MockTasks()))
+
+
+def use_llm(monkeypatch: pytest.MonkeyPatch, reply: str) -> None:
+    monkeypatch.setattr(med_qa_module, "get_gateway", lambda: LLMGateway(FixedPhraseTasks(reply)))
+
+
+def assert_safe(answer: MedAnswer) -> None:
+    assert not is_forbidden(answer.text)
+    assert "мг" not in answer.text  # never quotes dosing regimens
+    assert "врач" in answer.text  # doctor note always present
+
+
+# --- topic detection --------------------------------------------------------------------------------
+
+
+@pytest.mark.parametrize(
+    ("question", "topic"),
+    [
+        ("подташнивает с утра", "side_effects"),
+        ("опять тошнит", "side_effects"),
+        ("тошнота не проходит", "side_effects"),
+        ("от таблеток только хуже, брошу", "onset"),
+        ("когда подействует?", "onset"),
+        ("без пива не могу уснуть", "alcohol"),
+        ("можно ли выпить вина?", "alcohol"),
+        ("хочу бросить таблетки", "discontinuation"),
+        ("как принимать, до еды или после еды?", "administration"),
+        ("какая сегодня погода", None),
+    ],
+)
+def test_detect_topic(question: str, topic: str | None) -> None:
+    assert detect_topic(question) == topic
+
+
+async def test_explicit_topic_wins(info: FakeDrugInfo, mock_gateway: None) -> None:
+    # The classifier would say "discontinuation"; the engine decided the answer should be about onset.
+    answer = await med_qa("сертралин", S("брошу"), topic="onset", drug_info=info)
+    assert answer is not None and answer.section == "onset"
+
+
+async def test_engine_alias_topic(info: FakeDrugInfo, mock_gateway: None) -> None:
+    answer = await med_qa("сертралин", S("ну и что"), topic="alcohol_interaction", drug_info=info)
+    assert answer is not None and answer.section == "alcohol"
+
+
+# --- demo days ---------------------------------------------------------------------------------------
+
+
+async def test_day2_nausea(info: FakeDrugInfo, mock_gateway: None) -> None:
+    answer = await med_qa("сертралин", S("подташнивает с утра"), drug_info=info)
+    assert answer is not None
+    assert answer.section == "side_effects" and answer.source == SRC
+    assert "1–2 недель" in answer.text and "после еды" in answer.text
+    assert answer.phrased_by_llm is False  # mock echoes the excerpt
+    assert_safe(answer)
+
+
+async def test_day5_quit(info: FakeDrugInfo, mock_gateway: None) -> None:
+    answer = await med_qa("сертралин", S("от таблеток только хуже, брошу"), topic="quit_intent", drug_info=info)
+    assert answer is not None and answer.section == "onset"
+    assert "2–4 недели" in answer.text
+    assert "200" not in answer.text  # dosing sentence is dropped
+    assert "не прекращайте" in answer.text
+    assert_safe(answer)
+
+
+async def test_day10_alcohol(info: FakeDrugInfo, mock_gateway: None) -> None:
+    answer = await med_qa("сертралин", S("без пива не могу уснуть"), drug_info=info)
+    assert answer is not None and answer.section == "alcohol"
+    assert "не рекомендуется" in answer.text
+    assert_safe(answer)
+
+
+# --- retrieval -----------------------------------------------------------------------------------------
+
+
+def test_select_excerpt_picks_relevant_sentences() -> None:
+    section = DrugSection(inn="сертралин", section="side_effects", text=FAKE_SERTRALINE["side_effects"], source=SRC)
+    excerpt = select_excerpt(section, "подташнивает с утра")
+    assert excerpt is not None
+    assert "тошнот" in excerpt.lower()
+    assert "головная боль" not in excerpt
+
+
+def test_select_excerpt_none_when_not_relevant() -> None:
+    section = DrugSection(inn="сертралин", section="side_effects", text="Возможна сухость во рту.", source=SRC)
+    assert select_excerpt(section, "болит колено") is None
+
+
+async def test_missing_section_returns_none(mock_gateway: None) -> None:
+    info = FakeDrugInfo({"side_effects": FAKE_SERTRALINE["side_effects"]})
+    assert await med_qa("сертралин", S("можно пиво?"), drug_info=info) is None
+    assert "врач" in fallback_text()
+
+
+async def test_unknown_drug_and_unknown_topic(info: FakeDrugInfo, mock_gateway: None) -> None:
+    assert await med_qa("флуоксетин", S("тошнит"), drug_info=info) is None
+    assert await med_qa("сертралин", S("какая погода"), drug_info=info) is None
+
+
+async def test_default_registry_has_no_data(mock_gateway: None) -> None:
+    # Before lane B (#14) registers real excerpts the default NoDrugInfo returns nothing.
+    assert await med_qa("сертралин", S("тошнит")) is None
+
+
+async def test_drug_info_failure_returns_none(mock_gateway: None) -> None:
+    assert await med_qa("сертралин", S("тошнит"), drug_info=BrokenDrugInfo()) is None
+
+
+@pytest.mark.parametrize("question", ["можно увеличить дозу?", "можно пить 100 мг?", "можно пить полтаблетки?"])
+async def test_dose_questions_go_to_doctor(info: FakeDrugInfo, mock_gateway: None, question: str) -> None:
+    assert await med_qa("сертралин", S(question), topic="administration", drug_info=info) is None
+    assert info.calls == []
+
+
+# --- LLM rephrasing guard --------------------------------------------------------------------------
+
+
+async def test_forbidden_llm_output_falls_back_to_excerpt(info: FakeDrugInfo, monkeypatch: pytest.MonkeyPatch) -> None:
+    use_llm(monkeypatch, "Тошнота пройдёт. Уменьшите дозу сертралина вдвое.")
+    answer = await med_qa("сертралин", S("подташнивает с утра"), drug_info=info)
+    assert answer is not None and answer.phrased_by_llm is False
+    assert "Тошнота обычно возникает" in answer.text
+    assert_safe(answer)
+
+
+async def test_llm_adding_numbers_is_rejected(info: FakeDrugInfo, monkeypatch: pytest.MonkeyPatch) -> None:
+    use_llm(monkeypatch, "Тошнота обычно проходит за 3 дня.")
+    answer = await med_qa("сертралин", S("подташнивает с утра"), drug_info=info)
+    assert answer is not None and answer.phrased_by_llm is False
+    assert "3 дня" not in answer.text
+
+
+async def test_llm_inventing_content_is_rejected(info: FakeDrugInfo, monkeypatch: pytest.MonkeyPatch) -> None:
+    use_llm(monkeypatch, "Попейте имбирный чай, погуляйте вечером и съешьте кусочек лимона.")
+    answer = await med_qa("сертралин", S("подташнивает с утра"), drug_info=info)
+    assert answer is not None and answer.phrased_by_llm is False
+    assert "имбир" not in answer.text
+
+
+async def test_faithful_rephrasing_is_accepted(info: FakeDrugInfo, monkeypatch: pytest.MonkeyPatch) -> None:
+    reply = "Тошнота обычно бывает в первые дни приёма и проходит за 1–2 недели. Препарат принимают после еды."
+    use_llm(monkeypatch, reply)
+    answer = await med_qa("сертралин", S("подташнивает с утра"), drug_info=info)
+    assert answer is not None and answer.phrased_by_llm is True
+    assert reply in answer.text
+    assert_safe(answer)
+
+
+def test_adds_content() -> None:
+    excerpt = "Тошнота обычно проходит в течение 1–2 недель."
+    assert adds_content("Тошнота проходит за 5 недель.", excerpt, "тошнит") is True
+    assert adds_content("Тошнота обычно проходит в течение 1–2 недель.", excerpt, "тошнит") is False
+    assert adds_content("", excerpt, "тошнит") is True