feat(products): improve shopping suggestion decision support

backend/innercontext/api/products.py

@@ -257,11 +257,16 @@ class InventoryUpdate(SQLModel):
 class ProductSuggestion(PydanticBase):
     category: ProductCategory
     product_type: str
+    priority: Literal["high", "medium", "low"]
     key_ingredients: list[str]
     target_concerns: list[str]
-    why_needed: str
     recommended_time: DayTime
     frequency: str
+    short_reason: str
+    reason_to_buy_now: str
+    reason_not_needed_if_budget_tight: str | None = None
+    fit_with_current_routine: str
+    usage_cautions: list[str]
 
 
 class ShoppingSuggestionResponse(PydanticBase):
@@ -276,11 +281,16 @@ class ShoppingSuggestionResponse(PydanticBase):
 class _ProductSuggestionOut(PydanticBase):
     category: ProductCategory
     product_type: str
+    priority: Literal["high", "medium", "low"]
     key_ingredients: list[str]
     target_concerns: list[str]
-    why_needed: str
     recommended_time: DayTime
     frequency: str
+    short_reason: str
+    reason_to_buy_now: str
+    reason_not_needed_if_budget_tight: str | None = None
+    fit_with_current_routine: str
+    usage_cautions: list[str]
 
 
 class _ShoppingSuggestionsOut(PydanticBase):
@@ -982,6 +992,13 @@ ZASADY:
 8. Zwracaj uwagę na ewentualne konflikty polecanych składników z tymi, które użytkownik już posiada (np. nie polecaj peptydów miedziowych jeśli użytkownik nadużywa kwasów)
 9. Odpowiadaj w języku polskim
 10. Używaj wyłącznie dozwolonych wartości enumów poniżej - nie twórz synonimów typu "night", "evening" ani "treatment"
+11. Możesz zwrócić pustą listę suggestions, jeśli nie widzisz realnej potrzeby zakupowej
+12. Każda sugestia ma mieć charakter decision-support: konkretnie wyjaśnij, dlaczego warto kupić ją teraz, jak wpisuje się w obecną rutynę i jakie są ograniczenia
+13. `short_reason` ma być krótkim, 1-zdaniowym skrótem decyzji zakupowej
+14. `reason_to_buy_now` ma być konkretne i praktyczne, bez lania wody
+15. `reason_not_needed_if_budget_tight` jest opcjonalne - uzupełniaj tylko wtedy, gdy zakup nie jest pilny lub istnieje rozsądny kompromis
+16. `usage_cautions` ma być krótką listą praktycznych uwag; gdy brak istotnych zastrzeżeń zwróć pustą listę
+17. `priority` ustawiaj jako: high = wyraźna luka lub pilna potrzeba, medium = sensowne uzupełnienie, low = opcjonalny upgrade
 
 DOZWOLONE WARTOŚCI ENUMÓW:
 - category: "cleanser" | "toner" | "essence" | "serum" | "moisturizer" | "spf" | "mask" | "exfoliant" | "hair_treatment" | "tool" | "spot_treatment" | "oil"
@@ -1086,6 +1103,20 @@ def suggest_shopping(session: Session = Depends(get_session)):
     except json.JSONDecodeError as e:
         raise HTTPException(status_code=502, detail=f"LLM returned invalid JSON: {e}")
 
+    try:
+        parsed_response = _ShoppingSuggestionsOut.model_validate(parsed)
+    except ValidationError as exc:
+        formatted_errors = "; ".join(
+            f"{'/'.join(str(part) for part in err['loc'])}: {err['msg']}"
+            for err in exc.errors()
+        )
+        raise HTTPException(
+            status_code=502,
+            detail=(
+                f"LLM returned invalid shopping suggestion schema: {formatted_errors}"
+            ),
+        )
+
     # Get products with inventory (those user already owns)
     products_with_inventory_ids = session.exec(
         select(ProductInventory.product_id).distinct()
@@ -1102,8 +1133,11 @@ def suggest_shopping(session: Session = Depends(get_session)):
 
     # Build initial shopping response without metadata
     shopping_response = ShoppingSuggestionResponse(
-        suggestions=[ProductSuggestion(**s) for s in parsed.get("suggestions", [])],
+        suggestions=[
-        reasoning=parsed.get("reasoning", ""),
+            ProductSuggestion.model_validate(s.model_dump())
+            for s in parsed_response.suggestions
+        ],
+        reasoning=parsed_response.reasoning,
     )
 
     validation_result = validator.validate(shopping_response, shopping_context)

backend/innercontext/validators/shopping_validator.py

@@ -22,48 +22,9 @@ class ShoppingValidationContext:
 
 
 class ShoppingValidator(BaseValidator):
-    """Validates shopping suggestions for product types."""
+    """Validates shopping suggestion schema and copy quality."""
 
-    # Realistic product type patterns (not exhaustive, just sanity checks)
+    VALID_PRIORITIES = {"high", "medium", "low"}
-    VALID_PRODUCT_TYPE_PATTERNS = {
-        "serum",
-        "cream",
-        "cleanser",
-        "toner",
-        "essence",
-        "moisturizer",
-        "spf",
-        "sunscreen",
-        "oil",
-        "balm",
-        "mask",
-        "exfoliant",
-        "acid",
-        "retinoid",
-        "vitamin",
-        "niacinamide",
-        "hyaluronic",
-        "ceramide",
-        "peptide",
-        "antioxidant",
-        "aha",
-        "bha",
-        "pha",
-    }
-
-    VALID_FREQUENCIES = {
-        "daily",
-        "twice daily",
-        "am",
-        "pm",
-        "both",
-        "2x weekly",
-        "3x weekly",
-        "2-3x weekly",
-        "weekly",
-        "as needed",
-        "occasional",
-    }
 
     def validate(
         self, response: Any, context: ShoppingValidationContext
@@ -73,19 +34,17 @@ class ShoppingValidator(BaseValidator):
 
         Checks:
         1. suggestions field present
-        2. Product types are realistic (contain known keywords)
+        2. Categories are valid
-        3. Not suggesting products user already owns (should mark as [✗])
+        3. Targets are valid
-        4. Recommended frequencies are valid
+        4. Each suggestion has required fields
-        5. Categories are valid
+        5. Decision-support fields are well formed
-        6. Targets are valid
-        7. Each suggestion has required fields
 
         Args:
             response: Parsed shopping suggestion response
             context: Validation context
 
         Returns:
-            ValidationResult with any errors/warnings
+            ValidationResult with schema errors and lightweight quality warnings
         """
         result = ValidationResult()
 
@@ -112,15 +71,8 @@ class ShoppingValidator(BaseValidator):
                         f"Suggestion {sug_num}: invalid category '{suggestion.category}'"
                     )
 
-            # Check product type is realistic
+            if hasattr(suggestion, "priority") and suggestion.priority:
-            if hasattr(suggestion, "product_type") and suggestion.product_type:
+                self._check_priority_valid(suggestion.priority, sug_num, result)
-                self._check_product_type_realistic(
-                    suggestion.product_type, sug_num, result
-                )
-
-            # Check frequency is valid
-            if hasattr(suggestion, "frequency") and suggestion.frequency:
-                self._check_frequency_valid(suggestion.frequency, sug_num, result)
 
             # Check targets are valid
             if hasattr(suggestion, "target_concerns") and suggestion.target_concerns:
@@ -128,6 +80,11 @@ class ShoppingValidator(BaseValidator):
                     suggestion.target_concerns, sug_num, context, result
                 )
 
+            if hasattr(suggestion, "usage_cautions"):
+                self._check_usage_cautions(suggestion.usage_cautions, sug_num, result)
+
+            self._check_text_quality(suggestion, sug_num, result)
+
             # Check recommended_time is valid
             if hasattr(suggestion, "recommended_time") and suggestion.recommended_time:
                 if suggestion.recommended_time not in ("am", "pm", "both"):
@@ -142,7 +99,15 @@ class ShoppingValidator(BaseValidator):
         self, suggestion: Any, sug_num: int, result: ValidationResult
     ) -> None:
         """Check suggestion has required fields."""
-        required = ["category", "product_type", "why_needed"]
+        required = [
+            "category",
+            "product_type",
+            "priority",
+            "short_reason",
+            "reason_to_buy_now",
+            "fit_with_current_routine",
+            "usage_cautions",
+        ]
 
         for field in required:
             if not hasattr(suggestion, field) or getattr(suggestion, field) is None:
@@ -150,64 +115,14 @@ class ShoppingValidator(BaseValidator):
                     f"Suggestion {sug_num}: missing required field '{field}'"
                 )
 
-    def _check_product_type_realistic(
+    def _check_priority_valid(
-        self, product_type: str, sug_num: int, result: ValidationResult
+        self, priority: str, sug_num: int, result: ValidationResult
     ) -> None:
-        """Check product type contains realistic keywords."""
+        """Check priority uses supported enum values."""
-        product_type_lower = product_type.lower()
+        if priority not in self.VALID_PRIORITIES:
-
-        # Check if any valid pattern appears in the product type
-        has_valid_keyword = any(
-            pattern in product_type_lower
-            for pattern in self.VALID_PRODUCT_TYPE_PATTERNS
-        )
-
-        if not has_valid_keyword:
-            result.add_warning(
-                f"Suggestion {sug_num}: product type '{product_type}' looks unusual - "
-                "verify it's a real skincare product category"
-            )
-
-        # Check for brand names (shouldn't suggest specific brands)
-        suspicious_brands = [
-            "la roche",
-            "cerave",
-            "paula",
-            "ordinary",
-            "skinceuticals",
-            "drunk elephant",
-            "versed",
-            "inkey",
-            "cosrx",
-            "pixi",
-        ]
-
-        if any(brand in product_type_lower for brand in suspicious_brands):
             result.add_error(
-                f"Suggestion {sug_num}: product type contains brand name - "
+                f"Suggestion {sug_num}: invalid priority '{priority}' "
-                "should suggest product TYPES only, not specific brands"
+                "(must be 'high', 'medium', or 'low')"
-            )
-
-    def _check_frequency_valid(
-        self, frequency: str, sug_num: int, result: ValidationResult
-    ) -> None:
-        """Check frequency is a recognized pattern."""
-        frequency_lower = frequency.lower()
-
-        # Check for exact matches or common patterns
-        is_valid = (
-            frequency_lower in self.VALID_FREQUENCIES
-            or "daily" in frequency_lower
-            or "weekly" in frequency_lower
-            or "am" in frequency_lower
-            or "pm" in frequency_lower
-            or "x" in frequency_lower  # e.g. "2x weekly"
-        )
-
-        if not is_valid:
-            result.add_warning(
-                f"Suggestion {sug_num}: unusual frequency '{frequency}' - "
-                "verify it's a realistic usage pattern"
             )
 
     def _check_targets_valid(
@@ -227,3 +142,64 @@ class ShoppingValidator(BaseValidator):
                 result.add_error(
                     f"Suggestion {sug_num}: invalid target concern '{target}'"
                 )
+
+    def _check_usage_cautions(
+        self, usage_cautions: Any, sug_num: int, result: ValidationResult
+    ) -> None:
+        """Check usage cautions are a list of short strings."""
+        if not isinstance(usage_cautions, list):
+            result.add_error(f"Suggestion {sug_num}: usage_cautions must be a list")
+            return
+
+        for caution in usage_cautions:
+            if not isinstance(caution, str):
+                result.add_error(
+                    f"Suggestion {sug_num}: usage_cautions entries must be strings"
+                )
+                continue
+            if len(caution.strip()) > 180:
+                result.add_warning(
+                    f"Suggestion {sug_num}: usage caution is too long - keep it concise"
+                )
+
+    def _check_text_quality(
+        self, suggestion: Any, sug_num: int, result: ValidationResult
+    ) -> None:
+        """Warn when decision-support copy is too generic or empty-ish."""
+        generic_phrases = {
+            "wspiera skore",
+            "pomaga skorze",
+            "moze pomoc",
+            "dobry wybor",
+            "uzupelnia rutyne",
+            "supports the skin",
+            "may help",
+            "good option",
+            "complements the routine",
+        }
+
+        text_fields = [
+            ("short_reason", getattr(suggestion, "short_reason", None), 12),
+            ("reason_to_buy_now", getattr(suggestion, "reason_to_buy_now", None), 18),
+            (
+                "fit_with_current_routine",
+                getattr(suggestion, "fit_with_current_routine", None),
+                18,
+            ),
+        ]
+
+        for field_name, value, min_length in text_fields:
+            if not isinstance(value, str):
+                continue
+            stripped = value.strip()
+            if len(stripped) < min_length:
+                result.add_warning(
+                    f"Suggestion {sug_num}: {field_name} is very short - add more decision context"
+                )
+                continue
+
+            lowered = stripped.lower()
+            if lowered in generic_phrases:
+                result.add_warning(
+                    f"Suggestion {sug_num}: {field_name} is too generic - make it more specific"
+                )

backend/tests/test_products_helpers.py

@@ -5,17 +5,25 @@ from unittest.mock import patch
 from sqlmodel import Session
 
 from innercontext.api.products import (
+    ProductSuggestion,
+    ShoppingSuggestionResponse,
     _build_shopping_context,
     _extract_requested_product_ids,
     build_product_details_tool_handler,
 )
 from innercontext.models import (
     Product,
+    ProductCategory,
     ProductInventory,
     SexAtBirth,
+    SkinConcern,
     SkinConditionSnapshot,
 )
 from innercontext.models.profile import UserProfile
+from innercontext.validators.shopping_validator import (
+    ShoppingValidationContext,
+    ShoppingValidator,
+)
 
 
 def test_build_shopping_context(session: Session):
@@ -46,6 +54,7 @@ def test_build_shopping_context(session: Session):
     # Add product
     p = Product(
         id=uuid.uuid4(),
+        short_id=str(uuid.uuid4())[:8],
         name="Soothing Serum",
         brand="BrandX",
         category="serum",
@@ -108,7 +117,7 @@ def test_suggest_shopping(client, session):
             "Response",
             (),
             {
-                "text": '{"suggestions": [{"category": "cleanser", "product_type": "cleanser", "priority": "high", "key_ingredients": [], "target_concerns": [], "why_needed": "reason", "recommended_time": "am", "frequency": "daily"}], "reasoning": "Test shopping"}'
+                "text": '{"suggestions": [{"category": "cleanser", "product_type": "cleanser", "priority": "high", "key_ingredients": ["glycerin"], "target_concerns": ["dehydration"], "recommended_time": "am", "frequency": "daily", "short_reason": "Brakuje lagodnego kroku myjacego rano.", "reason_to_buy_now": "Obecnie nie masz delikatnego produktu do porannego oczyszczania i wsparcia bariery.", "reason_not_needed_if_budget_tight": "Mozesz tymczasowo ograniczyc sie do samego splukania twarzy rano, jesli skora jest spokojna.", "fit_with_current_routine": "To domknie podstawowy krok cleanse bez dokladania agresywnych aktywow.", "usage_cautions": ["unikaj mocnego domywania przy podraznieniu"]}], "reasoning": "Test shopping"}'
             },
         )
         mock_gemini.return_value = (mock_response, None)
@@ -118,6 +127,11 @@ def test_suggest_shopping(client, session):
         data = r.json()
         assert len(data["suggestions"]) == 1
         assert data["suggestions"][0]["product_type"] == "cleanser"
+        assert data["suggestions"][0]["priority"] == "high"
+        assert data["suggestions"][0]["short_reason"]
+        assert data["suggestions"][0]["usage_cautions"] == [
+            "unikaj mocnego domywania przy podraznieniu"
+        ]
         assert data["reasoning"] == "Test shopping"
         kwargs = mock_gemini.call_args.kwargs
         assert "USER PROFILE:" in kwargs["contents"]
@@ -133,9 +147,43 @@ def test_suggest_shopping(client, session):
         assert "get_product_details" in kwargs["function_handlers"]
 
 
+def test_suggest_shopping_invalid_json_returns_502(client):
+    with patch(
+        "innercontext.api.products.call_gemini_with_function_tools"
+    ) as mock_gemini:
+        mock_response = type("Response", (), {"text": "{"})
+        mock_gemini.return_value = (mock_response, None)
+
+        r = client.post("/products/suggest")
+
+        assert r.status_code == 502
+        assert "LLM returned invalid JSON" in r.json()["detail"]
+
+
+def test_suggest_shopping_invalid_schema_returns_502(client):
+    with patch(
+        "innercontext.api.products.call_gemini_with_function_tools"
+    ) as mock_gemini:
+        mock_response = type(
+            "Response",
+            (),
+            {
+                "text": '{"suggestions": [{"category": "cleanser", "product_type": "cleanser", "priority": "urgent", "key_ingredients": [], "target_concerns": [], "recommended_time": "am", "frequency": "daily", "short_reason": "x", "reason_to_buy_now": "y", "fit_with_current_routine": "z", "usage_cautions": []}], "reasoning": "Test shopping"}'
+            },
+        )
+        mock_gemini.return_value = (mock_response, None)
+
+        r = client.post("/products/suggest")
+
+        assert r.status_code == 502
+        assert "LLM returned invalid shopping suggestion schema" in r.json()["detail"]
+        assert "suggestions/0/priority" in r.json()["detail"]
+
+
 def test_shopping_context_medication_skip(session: Session):
     p = Product(
         id=uuid.uuid4(),
+        short_id=str(uuid.uuid4())[:8],
         name="Epiduo",
         brand="Galderma",
         category="serum",
@@ -162,6 +210,7 @@ def test_extract_requested_product_ids_dedupes_and_limits():
 def test_shopping_tool_handlers_return_payloads(session: Session):
     product = Product(
         id=uuid.uuid4(),
+        short_id=str(uuid.uuid4())[:8],
         name="Test Product",
         brand="Brand",
         category="serum",
@@ -176,10 +225,10 @@ def test_shopping_tool_handlers_return_payloads(session: Session):
     payload = {"product_ids": [str(product.id)]}
 
     details = build_product_details_tool_handler([product])(payload)
-    assert details["products"][0]["inci"] == ["Water", "Niacinamide"]
     assert details["products"][0]["actives"][0]["name"] == "Niacinamide"
     assert "context_rules" in details["products"][0]
     assert details["products"][0]["last_used_on"] is None
+    assert "inci" not in details["products"][0]
 
 
 def test_shopping_tool_handler_includes_last_used_on_from_mapping(session: Session):
@@ -200,3 +249,48 @@ def test_shopping_tool_handler_includes_last_used_on_from_mapping(session: Sessi
     )(payload)
 
     assert details["products"][0]["last_used_on"] == "2026-03-01"
+
+
+def test_shopping_validator_accepts_freeform_product_type_and_frequency():
+    response = ShoppingSuggestionResponse(
+        suggestions=[
+            ProductSuggestion(
+                category="spot_treatment",
+                product_type="Punktowy preparat na wypryski z ichtiolem lub cynkiem",
+                priority="high",
+                key_ingredients=["ichtiol", "cynk"],
+                target_concerns=["acne"],
+                recommended_time="pm",
+                frequency="Codziennie (punktowo na zmiany)",
+                short_reason="Pomaga opanowac aktywne zmiany bez dokladania pelnego aktywu na cala twarz.",
+                reason_to_buy_now="Brakuje Ci dedykowanego produktu punktowego na pojedyncze wypryski.",
+                fit_with_current_routine="Mozesz dolozyc go tylko na zmiany po serum lub zamiast mocniejszego aktywu.",
+                usage_cautions=["stosuj tylko miejscowo"],
+            ),
+            ProductSuggestion(
+                category="mask",
+                product_type="Lagodna maska oczyszczajaca",
+                priority="low",
+                key_ingredients=["glinka"],
+                target_concerns=["sebum_excess"],
+                recommended_time="pm",
+                frequency="1 raz w tygodniu",
+                short_reason="To opcjonalne wsparcie przy nadmiarze sebum.",
+                reason_to_buy_now="Moze pomoc domknac sporadyczne oczyszczanie, gdy skora jest bardziej przetluszczona.",
+                fit_with_current_routine="Najlepiej traktowac to jako dodatkowy krok, nie zamiennik podstaw rutyny.",
+                usage_cautions=[],
+            ),
+        ],
+        reasoning="Test",
+    )
+
+    result = ShoppingValidator().validate(
+        response,
+        ShoppingValidationContext(
+            owned_product_ids=set(),
+            valid_categories=set(ProductCategory),
+            valid_targets=set(SkinConcern),
+        ),
+    )
+
+    assert not any("unusual frequency" in warning for warning in result.warnings)

frontend/messages/en.json

@@ -58,6 +58,15 @@
   "products_suggestResults": "Suggestions",
   "products_suggestTime": "Time",
   "products_suggestFrequency": "Frequency",
+  "products_suggestPriorityHigh": "High priority",
+  "products_suggestPriorityMedium": "Medium priority",
+  "products_suggestPriorityLow": "Low priority",
+  "products_suggestBuyNow": "Buy now because",
+  "products_suggestRoutineFit": "How it fits your routine",
+  "products_suggestBudgetSkip": "If you're cutting the budget",
+  "products_suggestKeyIngredients": "Key ingredients",
+  "products_suggestTargets": "Targets",
+  "products_suggestCautions": "Cautions",
   "products_suggestRegenerate": "Regenerate",
   "products_suggestNoResults": "No suggestions.",
   "products_noProducts": "No products found.",

frontend/messages/pl.json

@@ -60,6 +60,15 @@
   "products_suggestResults": "Propozycje",
   "products_suggestTime": "Pora",
   "products_suggestFrequency": "Częstotliwość",
+  "products_suggestPriorityHigh": "Wysoki priorytet",
+  "products_suggestPriorityMedium": "Średni priorytet",
+  "products_suggestPriorityLow": "Niski priorytet",
+  "products_suggestBuyNow": "Kup teraz, bo",
+  "products_suggestRoutineFit": "Jak wpisuje się w rutynę",
+  "products_suggestBudgetSkip": "Jeśli tniesz budżet",
+  "products_suggestKeyIngredients": "Kluczowe składniki",
+  "products_suggestTargets": "Cele",
+  "products_suggestCautions": "Uwagi",
   "products_suggestRegenerate": "Wygeneruj ponownie",
   "products_suggestNoResults": "Brak propozycji.",
   "products_noProducts": "Nie znaleziono produktów.",

frontend/src/lib/types.ts

@@ -284,14 +284,21 @@ export interface BatchSuggestion {
 
 // ─── Shopping suggestion types ───────────────────────────────────────────────
 
+export type ShoppingPriority = 'high' | 'medium' | 'low';
+
 export interface ProductSuggestion {
   category: string;
   product_type: string;
+  priority: ShoppingPriority;
   key_ingredients: string[];
   target_concerns: string[];
-  why_needed: string;
   recommended_time: string;
   frequency: string;
+  short_reason: string;
+  reason_to_buy_now: string;
+  reason_not_needed_if_budget_tight?: string;
+  fit_with_current_routine: string;
+  usage_cautions: string[];
 }
 
 export interface ShoppingSuggestionResponse {

frontend/src/routes/products/suggest/+page.server.ts

@@ -17,6 +17,9 @@ export const actions: Actions = {
 			return {
 				suggestions: data.suggestions,
 				reasoning: data.reasoning,
+				validation_warnings: data.validation_warnings,
+				auto_fixes_applied: data.auto_fixes_applied,
+				response_metadata: data.response_metadata,
 			};
 		} catch (e) {
 			return fail(500, { error: String(e) });

frontend/src/routes/products/suggest/+page.svelte

@@ -1,7 +1,7 @@
 <script lang="ts">
 	import { enhance } from '$app/forms';
 	import { resolve } from '$app/paths';
-	import type { ProductSuggestion, ResponseMetadata } from '$lib/types';
+	import type { ProductSuggestion, ResponseMetadata, ShoppingPriority } from '$lib/types';
 	import { m } from '$lib/paraglide/messages.js';
 	import { Badge } from '$lib/components/ui/badge';
 	import { Button } from '$lib/components/ui/button';
@@ -22,6 +22,28 @@
 	let autoFixes = $state<string[] | undefined>(undefined);
 	let responseMetadata = $state<ResponseMetadata | undefined>(undefined);
 
+	function priorityLabel(priority: ShoppingPriority) {
+		switch (priority) {
+			case 'high':
+				return m.products_suggestPriorityHigh();
+			case 'medium':
+				return m.products_suggestPriorityMedium();
+			default:
+				return m.products_suggestPriorityLow();
+		}
+	}
+
+	function priorityClasses(priority: ShoppingPriority) {
+		switch (priority) {
+			case 'high':
+				return 'border-transparent bg-[color:var(--page-accent)] text-white';
+			case 'medium':
+				return 'border-[color:var(--page-accent)]/20 bg-[var(--page-accent-soft)] text-foreground';
+			default:
+				return 'border-border bg-secondary text-secondary-foreground';
+		}
+	}
+
 	function enhanceForm() {
 		loading = true;
 		errorMsg = null;
@@ -104,37 +126,93 @@
 		<div class="space-y-4 reveal-3">
 			<h3 class="text-lg font-semibold">{m["products_suggestResults"]()}</h3>
 			{#each suggestions as s (s.product_type)}
-				<Card>
+				<Card class="border-border/80 bg-card/95 shadow-sm">
 					<CardContent class="pt-4">
-						<div class="space-y-3">
+						<div class="space-y-4">
-							<div class="flex items-start justify-between gap-2">
+							<div class="flex flex-col gap-3 sm:flex-row sm:items-start sm:justify-between">
-								<h4 class="font-medium">{s.product_type}</h4>
+								<div class="space-y-2">
-								<Badge variant="secondary" class="shrink-0">{s.category}</Badge>
+									<div class="flex flex-wrap items-center gap-2">
+										<Badge class={priorityClasses(s.priority)}>{priorityLabel(s.priority)}</Badge>
+										<Badge variant="secondary" class="shrink-0">{s.category}</Badge>
+									</div>
+									<h4 class="font-medium tracking-[0.01em]">{s.product_type}</h4>
+									<p class="text-sm text-foreground/80">{s.short_reason}</p>
+								</div>
+
+								<div class="flex gap-4 text-xs text-muted-foreground sm:justify-end">
+									<span>{m["products_suggestTime"]()}: {s.recommended_time.toUpperCase()}</span>
+									<span>{m["products_suggestFrequency"]()}: {s.frequency}</span>
+								</div>
 							</div>
 
+							<div class="grid gap-3 md:grid-cols-2">
+								<div class="rounded-xl border border-border/70 bg-muted/20 p-3">
+									<p class="text-[11px] font-semibold uppercase tracking-[0.12em] text-muted-foreground">
+										{m.products_suggestBuyNow()}
+									</p>
+									<p class="mt-2 text-sm leading-6 text-foreground/85">{s.reason_to_buy_now}</p>
+								</div>
+
+								<div class="rounded-xl border border-border/70 bg-muted/20 p-3">
+									<p class="text-[11px] font-semibold uppercase tracking-[0.12em] text-muted-foreground">
+										{m.products_suggestRoutineFit()}
+									</p>
+									<p class="mt-2 text-sm leading-6 text-foreground/85">{s.fit_with_current_routine}</p>
+								</div>
+							</div>
+
+							{#if s.reason_not_needed_if_budget_tight}
+								<div class="rounded-xl border border-dashed border-border/80 bg-background p-3">
+									<p class="text-[11px] font-semibold uppercase tracking-[0.12em] text-muted-foreground">
+										{m.products_suggestBudgetSkip()}
+									</p>
+									<p class="mt-2 text-sm leading-6 text-foreground/80">
+										{s.reason_not_needed_if_budget_tight}
+									</p>
+								</div>
+							{/if}
+
 							{#if s.key_ingredients.length > 0}
-								<div class="flex flex-wrap gap-1">
+								<div class="space-y-2">
-									{#each s.key_ingredients as ing (ing)}
+									<p class="text-[11px] font-semibold uppercase tracking-[0.12em] text-muted-foreground">
-										<Badge variant="outline" class="text-xs">{ing}</Badge>
+										{m.products_suggestKeyIngredients()}
-									{/each}
+									</p>
+									<div class="flex flex-wrap gap-1.5">
+										{#each s.key_ingredients as ing (ing)}
+											<Badge variant="outline" class="text-[11px] normal-case tracking-normal">{ing}</Badge>
+										{/each}
+									</div>
 								</div>
 							{/if}
 
 							{#if s.target_concerns.length > 0}
-								<div class="flex flex-wrap gap-1">
+								<div class="space-y-2">
-									{#each s.target_concerns as concern (concern)}
+									<p class="text-[11px] font-semibold uppercase tracking-[0.12em] text-muted-foreground">
-										<Badge class="text-xs">{concern.replace(/_/g, ' ')}</Badge>
+										{m.products_suggestTargets()}
-									{/each}
+									</p>
+									<div class="flex flex-wrap gap-1.5">
+										{#each s.target_concerns as concern (concern)}
+											<Badge class="text-[11px] normal-case tracking-normal">{concern.replace(/_/g, ' ')}</Badge>
+										{/each}
+									</div>
 								</div>
 							{/if}
 
-							<p class="text-sm text-muted-foreground">{s.why_needed}</p>
+							{#if s.usage_cautions.length > 0}
-
+								<div class="space-y-2">
-							<div class="flex gap-4 text-xs text-muted-foreground">
+									<p class="text-[11px] font-semibold uppercase tracking-[0.12em] text-muted-foreground">
-								<span>{m["products_suggestTime"]()}: {s.recommended_time.toUpperCase()}</span>
+										{m.products_suggestCautions()}
-								<span>{m["products_suggestFrequency"]()}: {s.frequency}</span>
+									</p>
+									<div class="flex flex-wrap gap-1.5">
+										{#each s.usage_cautions as caution (caution)}
+											<Badge variant="outline" class="border-amber-200 bg-amber-50 text-[11px] normal-case tracking-normal text-amber-900">
+												{caution}
+											</Badge>
+										{/each}
+									</div>
+								</div>
+							{/if}
 							</div>
-						</div>
 					</CardContent>
 				</Card>
 			{/each}