Auto-suggest context-aware segments from entity via LLM

The cohort step now calls the LLM to suggest segments that match the
entity domain (dating profile → partner types, product → buyer personas,
resume → hiring managers). Replaces hardcoded product-focused defaults.

- Add /api/suggest-segments endpoint
- Auto-call on step 2 entry, with Re-suggest button for manual refresh

web/app.py

@@ -88,6 +88,11 @@ class CounterfactualConfig(BaseModel):
     parallel: int = 5
 
 
+class SuggestSegmentsInput(BaseModel):
+    entity_text: str
+    audience_context: str
+
+
 # ── Routes ────────────────────────────────────────────────────────────────
 
 @app.get("/")
@@ -134,6 +139,47 @@ async def get_session(sid: str):
     }
 
 
+@app.post("/api/suggest-segments")
+async def suggest_segments(input: SuggestSegmentsInput):
+    """Use LLM to suggest audience segments based on entity and context."""
+    client = get_client()
+    model = get_model()
+
+    prompt = f"""Given this entity and audience context, suggest 4-5 evaluator segments.
+Each segment should represent a distinct perspective that would evaluate this entity differently.
+
+Entity:
+{input.entity_text[:2000]}
+
+Audience context: {input.audience_context}
+
+Return JSON:
+{{
+    "segments": [
+        {{"label": "<concise segment description, 5-10 words>", "count": <6-10>}}
+    ]
+}}
+
+Make segments specific to THIS domain. For a product, use buyer personas. For a dating profile,
+use different types of potential partners. For a resume, use different hiring managers. Etc.
+Be concrete and relevant — no generic segments."""
+
+    try:
+        resp = client.chat.completions.create(
+            model=model,
+            messages=[{"role": "user", "content": prompt}],
+            response_format={"type": "json_object"},
+            max_tokens=1024,
+            temperature=0.7,
+        )
+        content = resp.choices[0].message.content
+        content = re.sub(r'<think>[\s\S]*?</think>', '', content).strip()
+        data = json.loads(content)
+        return data
+    except Exception as e:
+        raise HTTPException(500, f"Failed to suggest segments: {e}")
+
+
 @app.post("/api/cohort/generate")
 async def generate_cohort_endpoint(config: CohortConfig):
     """Generate an LLM cohort and attach to a new session."""

web/static/index.html

@@ -347,6 +347,7 @@
     <div id="segmentsList" class="segments-list"></div>
     <div class="btn-row">
       <button class="secondary" onclick="addSegment()">+ Add segment</button>
+      <button class="secondary" onclick="suggestSegments()">Re-suggest</button>
       <button onclick="generateCohort()" id="genCohortBtn">Generate cohort</button>
     </div>
 
@@ -593,16 +594,9 @@ async function init() {
     badge.className = 'config-badge warn';
   }
 
-  // Default segments
-  addSegment('Early adopter, tech-savvy', 8);
-  addSegment('Mainstream user, non-technical', 8);
-  addSegment('Budget-conscious comparison shopper', 8);
-  addSegment('Enterprise decision-maker', 8);
-
-  // Default changes
-  addChange('Add free tier', 'Introduce a generous free plan that lets users try core features with no credit card required.');
-  addChange('Add social proof', 'Display customer logos, case studies, and specific metrics (e.g., "Used by 5,000 teams") prominently on the page.');
-  addChange('Lower price by 40%', 'Reduce all paid plan prices by 40% across the board.');
+  // Changes will be populated with defaults
+  addChange('', '');
+  addChange('', '');
 }
 
 // ── Templates ──
@@ -656,6 +650,37 @@ async function saveEntity() {
   if (!desc.value) desc.value = `People evaluating: ${firstLine.replace(/^#+\s*/, '').trim()}`;
 
   goToStep(2);
+
+  // Auto-suggest segments via LLM
+  await suggestSegments();
+}
+
+async function suggestSegments() {
+  const entityText = document.getElementById('entityText').value.trim();
+  const audienceCtx = document.getElementById('cohortDesc').value.trim();
+  if (!entityText) return;
+
+  // Clear existing segments and show loading
+  const list = document.getElementById('segmentsList');
+  list.innerHTML = '<div style="color:var(--text2);font-size:0.85rem;padding:8px">Suggesting segments...</div>';
+
+  try {
+    const resp = await fetch('/api/suggest-segments', {
+      method: 'POST',
+      headers: {'Content-Type': 'application/json'},
+      body: JSON.stringify({entity_text: entityText, audience_context: audienceCtx}),
+    });
+    const data = await resp.json();
+    list.innerHTML = '';
+    (data.segments || []).forEach(seg => {
+      addSegment(seg.label, seg.count || 8);
+    });
+  } catch (e) {
+    // Fallback to empty segments on error
+    list.innerHTML = '';
+    addSegment('', 8);
+    addSegment('', 8);
+  }
 }
 
 // ── Step 2: Cohort ──